Sexual selection improves population fitness: a meta-analysis
Supplementary Material
Justin G. Cally1, Luke Holman1, Devi Stuart-Fox1
1The University of Melbourne
library(pander)
library(tidyr)
library(compute.es)
library(metafor)
library(plyr)
library(dplyr)
library(lme4)
library(car)
library(forestplot)
library(ggplot2)
library(ggthemes)
library(kableExtra)
library(ggrepel)
library(reshape2)
library(RColorBrewer)
library(ggridges)
library(rstan) #Note that installation requires some effort: dependency for brms
library(brms)
library(backports) #seems to be a dependency
library(bayesplot)
#devtools::install_github("mvuorre/brmstools")
library(brmstools)
library(metaAidR)
source("I2_function.R") #Adapted function for obtaining I2 with CIs
source("Vdodge_function.R") # nice function for ggplot
source("Tidy_functions_for_brms.R") #Tidy functions for making model tables for brmsSupplementary Methods
Our aim was to investigate the effects of sexual selection on population fitness by conducting a meta-analysis on studies that measured fitness related outcomes after experimentally evolving a population under varying levels of opportunity for sexual selection. Here we describe the process of the literature search, data extraction, effect size calculation, formulation of multilevel models and assessing publication bias. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) as a guide during this meta-analysis.
Literature Search
The literature search was conducted under the following conditions:
We searched ISI Web of Science and Scopus on 9th June 2017. The two search engines produded a somewhat different set of papers (see PRISMA Figure in manuscript).
Studies were restricted to those from peer-reviewed and in the English language.
We devised a search strategy that sought to find studies which manipulated the presence or strength of sexual selection using experimental evolution, and then measured some proxy of population fitness. As such the search terms were as follows:
ISI Web of Science
We used the following search on ISI Web of Science:
Topic (TS) = “Sexual Selection” OR Promisc* OR Monogam* OR Polygam* OR Polyandr* OR Polygyn* OR “Mate choice”
AND
Topic (TS) = Fitness OR “Population Fitness” OR Deleterious OR “Male Strength” OR Fecund* OR Viability OR Productiv* OR “Reproductive Success” OR “Reproductive Rate” OR Surviv* OR | “Development Rate” OR Extinct* OR “Competitive Success” OR Mortality OR Mass OR “Body Size” OR “Wing Size” OR Emergence OR Mating Rate OR “Mating Propensity” OR Adapt* OR “Novel | Environment” OR “Sexual Conflict” OR “Sexual Antagonis*”
AND
Topic (TS) = Generations OR “Experimental evolution” OR “mutation load”
AND
Research Area (SU) = “Evolutionary Biology”
Scopus
We used the following search on Scopus:
TITLE-ABS-KEY = “Sexual Selection” OR Promisc* OR Monogam* OR Polygam* OR Polyandr* OR Polygyn* OR “Mate choice”
AND
TITLE-ABS-KEY = Fitness OR “Population Fitness” OR Deleterious OR “Male Strength” OR Fecund* OR Viability OR Productiv* OR “Reproductive Success” OR “Reproductive Rate” OR Surviv* | OR “Development Rate” OR Extinct* OR “Competitive Success” OR Mortality OR Mass OR “Body Size” OR “Wing Size” OR Emergence OR Mating Rate OR “Mating Propensity” OR Adapt* OR | “Novel Environment” OR “Sexual Conflict” OR “Sexual Antagonis*”
AND
TITLE-ABS-KEY = Generations OR “Experimental evolution” OR “mutation load”
Additions to the Literature Search
In addition to studies found from the literature search we also included three relevant studies that we found, which were not picked up in the subsequent formal searches (Partridge 1980; Price, Hurst, and Wedell 2010; Savic Veselinovic et al. 2013) see PRISMA Figure in manuscript.
Inclusion/Exclusion criteria
After removing duplicates papers recovered from both Web of Science and Scopus, we read the titles and abstracts of the remaining 1015 papers, and removed papers that were not relevant (typically because they were not an empirical study using experimental evolution). This left 130 papers, for which we read the full text and applied the following selection criteria:
- (1: Study Design) The study was an experimental evolution study lasting >1 generation
- (2: Population) a) The study was conducted using an animal species that was b) dioecious
- (3: Intervention and Control) The study experimentally manipulated the strength of sexual selection for at least one generation (e.g. via enforced monogamy or an altered sex ratio)
- (4: Outcomes) The study measured a trait that we judged to be a potential correlate of population fitness.
This latter criterion is likely to be contentious, because there is rarely enough data justify the assumption that a particular trait is (or is not) correlated with population fitness. We therefore relied on our best judgement when deciding which studies to exclude (see Table S1). The inclusion/exlusion critera as applied to each study are detailed in Table S2.
Table S1: We classed each of the twenty fitness related outcomes into three broad groups of direct, indirect and ambiguous based on the established link with population fitness, the directionality of the measure. Here we detailed how these outcomes were measured in the studies of this meta-analysis. In the accompanying box we provide a legend to the references cited in the table.
outcome.descriptions <- read.csv('data/outcome.descriptions.csv',
fileEncoding="UTF-8")
kable(outcome.descriptions, "html") %>%
kable_styling() %>%
scroll_box(width = "100%", height = "500px")| Outcome | Classification | Explanation |
|---|---|---|
| Behavioural Plasticity | Ambiguous | Female kicking against male harassment in different sociosexual contexts for the beetle Callosobruchus maculatus (1) |
| Body Size | Ambiguous | Body size was often recorded to correct for other morphometric traits (e.g. body condition, strength or testes weight (2, 3). It was measured as either length or dry mass. |
| Development Rate | Ambiguous | Egg-to adult development time was recorded in several studies (4-6) and often alongside traits other life-history traits suspected to impact fitness. |
| Early Fecundity | Ambiguous | Early fecundity was measured (alongside lifetime fecundity) as a life-history trait that may impact lifetime reproductive success. It was defined as either the total or proportional reproductive output in earlier stages of maturity (e.g. within the first 7 days) (7, 8). |
| Immunity | Ambiguous | Phenoloxidase (PO) activity or parasite load (6, 9-11). |
| Mating Duration | Ambiguous | Mating duration may have variable fitness impacts based on the soiciosexual conditions and extent of sexual conflict. It may be beneficial to have longer mating bouts for a male in a competitive environment however it may be damaging for a female under benign conditions (e.g. 1, 12, 13, 14). |
| Pesticide Resistance | Ambiguous | Pesticide resistance was measured both in the presence and absence of pesticides for the insect Tribolium castaneum, it was a binary measure of resistance to knockdown that was incorporated into generalized linear mixed models (15), |
| Mutant Frequency | Indirect | Allele and mutant frequency measured at the population level (Arbuthnott and Rundle (16), Hollis, Fierst (17)) |
| Body Condition | Indirect | Mean body weight of Onthophagus Taurus adjusted for body size (thorax width) (Simmons and Garcia-Gonzalez (2). |
| Fitness Senescence | Indirect | Rate of decline in survival probability across lifespan (5, 18). |
| Lifespan | Indirect | Longevity or survival across the entire lifespan (e.g. 19) or from a given point once under stressful conditions, such as starvation or after females mated in different operational sex ratios (e.g. 20). |
| Male Attractiveness | Indirect | Inferred from female preference tests in mice (21, 22) and male ornament size (coloration) in guppies (23). |
| Mating Frequency | Indirect | Number of mounts by males on females in Tribolium castaneum and Drosophila melanogaster (13, 19). |
| Mating Latency | Indirect | Time taken for a male to undertake their first copulatory mount from the time of being first put together with female/s (1, 12-14, 24, 25). |
| Mating Success | Indirect | Male mating success measured males ability to successfully mate with females. Often in the presence of other males (e.g. 8, 24, 26). Mating success of a male against a rival male can be determined via competing a focal male against an irradiated (infertile) competitor, the resulting proportion of eggs hatching are then determined to be a measure of the focal males success. Mating success also included measurements of mating capacity where males were continually presented with females until exhausted, the number of sequential matings were then recorded (27) and mating offence and defence ability (14). The mating offence and defence capability was estimated via paternity share of a male when in the first mating position (P1) or the second (P2). |
| Strength | Indirect | Male pulling strength in the dung beetle, Onthophagus Taurus, measured by attaching weights and measuring the weight the beetle was able to pull (Almbro and Simmons (3) . |
| Ejaculate Quality and Production | Indirect | Sperm quality and production grouped multiple measured outcomes together, both within a study (28) and during the meta-analysis. This includes sperm size, plug size, testes size, soporific effect, ejaculate weight, accessory gland size, motility, path velocity, sperm longevity (e.g. 1, 29, 30, 31) |
| Extinction Rate | Direct | Extinction rate was measured at the population level, either via recording the proportion of extinct lines after a given number of generations (32, 33) or via analysis of extinction rate over consecutive generations via the Weibull baseline hazard distribution (34). |
| Offspring Viability | Direct | Offspring viability, also recorded as egg-to-adult viability or embryonic viability, was measured as survival to a certain age (e.g. 1 year (23)) or life stage (e.g. hatching (35)). |
| Reproductive Success | Direct | A measure of the number of offspring produced by an individual. Reproductive success was also described as fecundity (e.g. 36), number of offspring produced (e.g. 37), fertility (e.g. 12) in females and proportion or total progeny sired in males (e.g. 5). |
outcome.references <- read.csv('data/references.tableS1.csv',
fileEncoding="UTF-8")
kable(outcome.references, "html") %>%
kable_styling() %>%
scroll_box(width = "100%", height = "250px")| Citation | Reference |
|---|---|
| 1 | van Lieshout E, McNamara KB, Simmons LW. Rapid Loss of Behavioral Plasticity and Immunocompetence under Intense Sexual Selection. Evolution. 2014;68(9):2550-8. |
| 2 | Simmons LW, Garcia-Gonzalez F. Evolutionary Reduction in Testes Size and Competitive Fertilization Success in Response to the Experimental Removal of Sexual Selection in Dung Beetles. Evolution. 2008;62(10):2580-91. |
| 3 | Almbro M, Simmons LW. Sexual Selection Can Remove an Experimentally Induced Mutation Load. Evolution. 2014;68(1):295-300. |
| 4 | Fricke C, Arnqvist G. Rapid adaptation to a novel host in a seed beetle (Callosobruchus maculatus): The role of sexual selection. Evolution. 2007;61(2):440-54. |
| 5 | Hollis, B. and T. J. Kawecki. 2014. Male cognitive performance declines in the absence of sexual selection. Proc. R. Soc. B-Biol. Sci. 281. |
| 6 | McKean KA, Nunney L. Sexual selection and immune function in Drosophila melanogaster. Evolution. 2008;62(2):386-400. |
| 7 | Crudgington HS, Fellows S, Snook RR. Increased opportunity for sexual conflict promotes harmful males with elevated courtship frequencies. Journal of Evolutionary Biology. 2010;23(2):440-6. |
| 8 | Tilszer, M. Antoszczyk, K. Salek, N. Zajac, E. Radwan, J.. Evolution under relaxed sexual conflict in the bulb mite Rhizoglyphus robini. Evolution. 2006;60(9):1868-73. |
| 9 | Hangartner S, Michalczyk L, Gage MJG, Martin OY. Experimental removal of sexual selection leads to decreased investment in an immune component in female Tribolium castaneum. Infection, Genetics and Evolution. 2015;33:212-8. |
| 10 | Hangartner S, Sbilordo SH, Michalczyk L, Gage MJG, Martin OY. Are there genetic trade-offs between immune and reproductive investments in Tribolium castaneum? Infection, Genetics and Evolution. 2013;19:45-50. |
| 11 | McNamara KB, van Lieshout E, Simmons LW. A test of the sexy-sperm and good-sperm hypotheses for the evolution of polyandry. Behavioral Ecology. 2014;25(4):989-95. |
| 12 | Edward DA, Fricke C, Chapman T. Adaptations to sexual selection and sexual conflict: insights from experimental evolution and artificial selection. Philosophical Transactions of the Royal Society B-Biological Sciences. 2010;365(1552):2541-8. |
| 13 | Michalczyk L, Millard AL, Martin OY, Lumley AJ, Emerson BC, Gage MJG. Experimental Evolution Exposes Female and Male Responses to Sexual Selection and Conflict in Tribolium Castaneum. Evolution. 2011;65(3):713-24. |
| 14 | Nandy B, Chakraborty P, Gupta V, Ali SZ, Prasad NG. Sperm Competitive Ability Evolves in Response to Experimental Alteration of Operational Sex Ratio. Evolution. 2013;67(7):2133-41. |
| 15 | Jacomb F, Marsh J, Holman L. Sexual selection expedites the evolution of pesticide resistance. Evolution. 2016;70(12):2746-51. |
| 16 | Arbuthnott D, Rundle HD. Sexual Selection Is Ineffectual or Inhibits the Purging of Deleterious Mutations in Drosophila Melanogaster. Evolution. 2012;66(7):2127-37. |
| 17 | Hollis B, Fierst JL, Houle D. Sexual Selection Accelerates the Elimination of a Deleterious Mutant in Drosophila Melanogaster. Evolution. 2009;63(2):324-33. |
| 18 | Archer CR, Duffy E, Hosken DJ, Mokkonen M, Okada K, Oku K, et al. Sex-specific effects of natural and sexual selection on the evolution of life span and ageing in Drosophila simulans. Functional Ecology. 2015;29(4):562-9. |
| 19 | Wigby S, Chapman T. Female resistance to male harm evolves in response to manipulation of sexual conflict. Evolution. 2004;58(5):1028-37. |
| 20 | Martin OY, Hosken DJ. Costs and benefits of evolving under experimentally enforced polyandry or monogamy. Evolution. 2003;57(12):2765-72. |
| 21 | Firman RC. Female social preference for males that have evolved via monogamy: evidence of a trade-off between pre- and post-copulatory sexually selected traits? Biology Letters. 2014;10(10). |
| 22 | Nelson AC, Colson KE, Harmon S, Potts WK. Rapid adaptation to mammalian sociality via sexually selected traits. Bmc Evolutionary Biology. 2013;13. |
| 23 | Pelabon C, Larsen LK, Bolstad GH, Viken A, Fleming IA, Rosenqvist G. The effects of sexual selection on life-history traits: An experimental study on guppies. Journal of Evolutionary Biology. 2014;27(2):404-16. |
| 24 | Debelle A, Ritchie MG, Snook RR. Sexual selection and assortative mating: an experimental test. Journal of Evolutionary Biology. 2016;29(7):1307-16. |
| 25 | Hollis B, Kawecki TJ. Male cognitive performance declines in the absence of sexual selection. Proceedings of the Royal Society B-Biological Sciences. 2014;281(1781). |
| 26 | McGuigan K, Petfield D, Blows MW. Reducing mutation load through sexual selection on males. Evolution. 2011;65(10):2816-29. |
| 27 | Crudgington HS, Fellows S, Badcock NS, Snook RR. Experimental Manipulation of Sexual Selection Promotes Greater Male Mating Capacity but Does Not Alter Sperm Investment. Evolution. 2009;63(4):926-38. |
| 28 | Firman RC, Simmons LW. Experimental Evolution of Sperm Quality Via Postcopulatory Sexual Selection in House Mice. Evolution. 2010;64(5):1245-56. |
| 29 | Fritzsche K, Timmermeyer N, Wolter M, Michiels NK. Female, but not male, nematodes evolve under experimental sexual coevolution. Proceedings of the Royal Society B-Biological Sciences. 2014;281(1796). |
| 30 | Gay L, Hosken DJ, Vasudev R, Tregenza T, Eady PE. Sperm competition and maternal effects differentially influence testis and sperm size in Callosobruchus maculatus. Journal of Evolutionary Biology. 2009;22(5):1143-50. |
| 31 | McNamara KB, Robinson SP, Rosa ME, Sloan NS, van Lieshout E, Simmons LW. Male-biased sex ratio does not promote increased sperm competitiveness in the seed beetle, Callosobruchus maculatus. Scientific Reports. 2016;6. |
| 32 | Jarzebowska M, Radwan J. Sexual Selection Counteracts Extinction of Small Populations of the Bulb Mites. Evolution. 2010;64(5):1283-9. |
| 33 | Plesnar-Bielak A, Skrzynecka AM, Prokop ZM, Radwan J. Mating system affects population performance and extinction risk under environmental challenge. Proceedings of the Royal Society B-Biological Sciences. 2012;279(1747):4661-7. |
| 34 | Lumley AJ, Michalczyk L, Kitson JJN, Spurgin LG, Morrison CA, Godwin JL, et al. Sexual selection protects against extinction. Nature. 2015;522(7557):470-+. |
| 35 | Plesnar A, Konior M, Radwan J. The role of sexual selection in purging the genome of induced mutations in the bulb mite (Rizoglyphus robini). Evolutionary Ecology Research. 2011;13(2):209-16. |
| 36 | Firman RC. Polyandrous females benefit by producing sons that achieve high reproductive success in a competitive environment. Proceedings of the Royal Society B-Biological Sciences. 2011;278(1719):2823-31. |
| 37 | Bernasconi G, Keller L. Female polyandry affects their sons’ reproductive success in the red flour beetle Tribolium castaneum. Journal of Evolutionary Biology. 2001;14(1):186-93. |
Table S2: An eligibility criteria was based on four features a study needed to include (discussed above), to be eligable for inclusion in the meta-analysis the study needed to satisfy all criteria. Here we applied a step-wise process to the studies that had their full-text read and excluded them when they first failed to meet the criteria. Additional notes documenting reasons behind exclusion were also taken.
Eligibility.criteria <- read.csv('data/Eligibility Workbook(22.02).csv',
fileEncoding="UTF-8")
kable(Eligibility.criteria, "html") %>%
kable_styling() %>%
scroll_box(width = "100%", height = "500px")| Authors | Year | Title | Study.Design | Population | Intervention.and.Control | Outcomes | Included | Exclusion.Reason | Notes |
|---|---|---|---|---|---|---|---|---|---|
| Aguirre, J. D. and D. J. Marshall | 2012 | Does Genetic Diversity Reduce Sibling Competition? | No | No | 1 | Not an experimental evolution study: full-sib/half-sib breeding design | |||
| Ahuja, A. and R. S. Singh | 2008 | Variation and evolution of male sex combs in Drosophila: Nature of selection response and theories of genetic variation for sexual traits | No | No | 1 | Artificial selection was conducted | |||
| Almbro, M. and L. W. Simmons | 2014 | Sexual Selection Can Remove an Experimentally Induced Mutation Load | Yes | Yes | Yes | Yes | Yes | Male strength is important in male-male competition | |
| Amitin, E. G. and S. Pitnick | 2007 | Influence of developmental environment on male- and female-mediated sperm precedence in Drosophila melanogaster | Yes | Yes | No | No | 3 | Larval density was the intervention: not strength of sexual selection | |
| Antolin, M. F., P. J. Ode, G. E. Heimpel, R. B. O’Hara and M. R. Strand | 2003 | Population structure, mating system, and sex-determining allele diversity of the parasitoid wasp Habrobracon hebetor | No | No | 1 | Not experimental evolution: Lab rearing of wild populations with eventual genetic analysis | |||
| Arbuthnott, D., E. M. Dutton, A. F. Agrawal and H. D. Rundle | 2014 | The ecology of sexual conflict: ecologically dependent parallel evolution of male harm and female resistance in Drosophila melanogaster | Yes | Yes | No | No | 3 | Intervention was either ethanol or cadmium mixture | |
| Arbuthnott, D. and H. D. Rundle | 2012 | Sexual Selection Is Ineffectual or Inhibits the Purging of Deleterious Mutations in Drosophila Melanogaster | Yes | Yes | Yes | Yes | Yes | Natural selection acted against known deleterious alleles, thus indicate fitness aspect | |
| Arbuthnott, D. and H. D. Rundle | 2014 | Misalignment of natural and sexual selection among divergently adapted Drosophila melanogaster populations | Yes | Yes | No | No | 3 | Intervention was either ethanol or cadmium mixture | |
| Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Sex-specific effects of natural and sexual selection on the evolution of life span and ageing in Drosophila simulans | Yes | Yes | Yes | Yes | Yes | Natural selection was measured simultanous and thus provides measurement of suitability of phenotype to environment | |
| Artieri, C. G., W. Haerty, B. P. Gupta and R. S. Singh | 2008 | Sexual selection and maintenance of sex: Evidence from comparisons of rates of genomic accumulation of mutations and divergence of sex-related genes in sexual and hermaphroditic species of Caenorhabditis | No | No | 1 | Comparative genomic approach | |||
| Bacigalupe, L. D., H. S. Crudgington, F. Hunter, A. J. Moore and R. R. Snook | 2007 | Sexual conflict does not drive reproductive isolation in experimental populations of Drosophila pseudoobscura | Yes | Yes | Yes | No | No | 4 | Viability and sterility were measured as well as mating speed, however these were in crosses, refer to 2008 study for beater outcomes |
| Bacigalupe, L. D., H. S. Crudgington, J. Slate, A. J. Moore and R. R. Snook | 2008 | Sexual selection and interacting phenotypes in experimental evolution: A study of Drosophila pseudoobscura mating behavior | Yes | Yes | Yes | Yes | No | Data not suitable | Mating speed cited as a measure of fitness. Because of the crossses the data is not able to be extracted to an effect size that is comprable to other studies |
| Barbosa, M., S. R. Connolly, M. Hisano, M. Dornelas and A. E. Magurran | 2012 | Fitness consequences of female multiple mating: A direct test of indirect benefits | No | No | 1 | Measures multiple mating not experimental evolution with sexual selection treatments | |||
| Bernasconi, G. and L. Keller | 2001 | Female polyandry affects their sons’ reproductive success in the red flour beetle Tribolium castaneum | Yes | Yes | Yes | Yes | Yes | Polyandry was done sequentially with postcop mate choice. | |
| Bielak, A. P., A. M. Skrzynecka, K. Miler and J. Radwan | 2014 | Selection for alternative male reproductive tactics alters intralocus sexual conflict | No | No | 1 | Artificial selection was conducted | |||
| Blows, M. W. | 2002 | Interaction between natural and sexual selection during the evolution of mate recognition | Yes | Yes | Yes | No | No | 4 | Hybrid Drosophilia used, indirect fitness was measured (mate recognition system) |
| Brommer, J. E., C. Fricke, D. A. Edward and T. Chapman | 2012 | Interactions between Genotype and Sexual Conflict Environment Influence Transgenerational Fitness in Drosophila Melanogaster | Yes | Yes | Yes | Yes | Yes | Multiple males but only one at a time: still is post copulatory SS, so included | |
| Castillo, D. M., M. K. Burger, C. M. Lively and L. F. Delph | 2015 | Experimental evolution: Assortative mating and sexual selection, independent of local adaptation, lead to reproductive isolation in the nematode Caenorhabditis remanei | Yes | Yes | No | No | 3 | No SS lines | |
| Cayetano, L., A. A. Maklakov, R. C. Brooks and R. Bonduriansky | 2011 | Evolution of Male and Female Genitalia Following Release from Sexual Selection | Yes | Yes | Yes | No | No | 4 | Conflict / burdensome and defensive / offensive traits have fitness costs and benefits: Removing as too difficult to see clear fitness of measurements |
| Chandler, C. H., C. Ofria and I. Dworkin | 2013 | Runaway Sexual Selection Leads to Good Genes | Yes | No | No | 2a | Digital organisms used | ||
| Chenoweth, S. F., N. C. Appleton, S. L. Allen and H. D. Rundle | 2015 | Genomic Evidence that Sexual Selection Impedes Adaptation to a Novel Environment | Yes | Yes | Yes | No | No | 4 | Alongside direct fitness, SNPs also used. This paper reports SNPs while Rundle (2006) reports fitness measures. Thus data is extracted from that paper, not this one |
| Chenoweth, S. F., D. Petfield, P. Doughty and M. W. Blows | 2007 | Male choice generates stabilizing sexual selection on a female fecundity correlate | No | No | 1 | Behavioural mate choice experiment | |||
| Chenoweth, S. F., H. D. Rundle and M. W. Blows | 2008 | Genetic constraints and the evolution of display trait sexual dimorphism by natural and sexual selection | Yes | Yes | Yes | No | No | 4 | Natural selection was also measured and CHCs provide an indirect fitness aspect but too difficult to compare (CHCs were not used as outcome in this meta-analysis) |
| Chenoweth, S. F., H. D. Rundle and M. W. Blows | 2010 | Experimental evidence for the evolution of indirect genetic effects: changes in the interaction effect coefficient, psi (_), due to sexual selection | Yes | Yes | Yes | No | No | 4 | CHCs may provide indirect fitness aspect but are very difficult measures to compare or turn into effect sizes |
| Crudgington, H. S., A. P. Beckerman, L. Brustle, K. Green and R. R. Snook | 2005 | Experimental removal and elevation of sexual selection: Does sexual selection generate manipulative males and resistant females? | Yes | Yes | Yes | Yes | Yes | ||
| Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Experimental Manipulation of Sexual Selection Promotes Greater Male Mating Capacity but Does Not Alter Sperm Investment | Yes | Yes | Yes | Yes | Yes | Appears to measure more direct and indirect outcomes | |
| Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Increased opportunity for sexual conflict promotes harmful males with elevated courtship frequencies | Yes | Yes | Yes | Yes | Yes | ||
| Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Sexual selection and assortative mating: an experimental test | Yes | Yes | Yes | Yes | Yes | ||
| Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Experimental Removal of Sexual Selection Reveals Adaptations to Polyandry in Both Sexes | Yes | Yes | Yes | Yes | Yes | ||
| Edward, D. A., C. Fricke and T. Chapman | 2010 | Adaptations to sexual selection and sexual conflict: insights from experimental evolution and artificial selection | Yes | Yes | Yes | Yes | Yes | ||
| Fava, G. | 1975 | Studies on the selective agents operating in experimental populations of Tisbe clodiensis (Copepoda, Harpacticoida) | Yes | Yes | No | No | 3 | No difference in SS between treatments: Instead different genotype frequencies. | |
| Firman, R. C. | 2011 | Polyandrous females benefit by producing sons that achieve high reproductive success in a competitive environment | Yes | Yes | Yes | Yes | Yes | It looks like post copulatory selection was used here | |
| Firman, R. C. | 2014 | Female social preference for males that have evolved via monogamy: evidence of a trade-off between pre- and post-copulatory sexually selected traits? | Yes | Yes | Yes | Yes | Yes | The outcome measured was female preference and male scent marking rate. Likely to have a role in fitness but not explicitly stated | |
| Firman, R. C., L. Y. Cheam and L. W. Simmons | 2011 | Sperm competition does not influence sperm hook morphology in selection lines of house mice | Yes | Yes | Yes | Yes | Yes | Sperm quality was measured | |
| Firman, R. C., F. Garcia-Gonzalez, E. Thyer, S. Wheeler, Z. Yamin, M. Yuan and L. W. Simmons | 2015 | Evolutionary change in testes tissue composition among experimental populations of house mice | Yes | Yes | Yes | Yes | Yes | Amount of sperm producing tissue was measured as it provides an advantage in sperm competition | |
| Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | The Coevolution of Ova Defensiveness with Sperm Competitiveness in House Mice | Yes | Yes | Yes | Yes | Yes | Ova defensivenenss can bias fertilization to a more specific type of sperm and thus be a fitness adavantage | |
| Firman, R. C. and L. W. Simmons | 2010 | Experimental Evolution of Sperm Quality Via Postcopulatory Sexual Selection in House Mice | Yes | Yes | Yes | Yes | Yes | Polygamous lines have only post-copulatory selection | |
| Firman, R. C. and L. W. Simmons | 2011 | Experimental evolution of sperm competitiveness in a mammal | Yes | Yes | Yes | Yes | Yes | Sperm competition is a fitness advantage | |
| Firman, R. C. and L. W. Simmons | 2012 | Male house mice evolving with post-copulatory sexual selection sire embryos with increased viability | Yes | Yes | Yes | Yes | Yes | Post cop SS used | |
| Fricke, C., C. Andersson and G. Arnqvist | 2010 | Natural selection hampers divergence of reproductive traits in a seed beetle | Yes | Yes | Yes | No | No | 4 | Could not use the broad outcome of reproductive characteristics as it is not directional |
| Fricke, C. and G. Arnqvist | 2007 | Rapid adaptation to a novel host in a seed beetle (Callosobruchus maculatus): The role of sexual selection | Yes | Yes | Yes | Yes | Yes | Post cop SS used | |
| Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Sex Ratio Bias Leads to the Evolution of Sex Role Reversal in Honey Locust Beetles | Yes | Yes | Yes | Yes | Yes | Male bias and female bias setups without monogamus/lack of SS | |
| Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Female, but not male, nematodes evolve under experimental sexual coevolution | Yes | Yes | Yes | Yes | Yes | Male bias and female bias setups without monogamus/lack of SS | |
| Garcia-Gonzalez, F., Y. Yasui and J. P. Evans | 2015 | Mating portfolios: bet-hedging, sexual selection and female multiple mating | Yes | Yes | Yes | Yes | No | Data not suitable | Experiments run alongside bet-hedging, perhaps confounding and not able to be placed alongside other studies in this meta-analysis |
| Gay, L., P. E. Eady, R. Vasudev, D. J. Hosken and T. Tregenza | 2009 | Does reproductive isolation evolve faster in larger populations via sexually antagonistic coevolution? | Yes | Yes | No | No | 3 | Generations of monoandry were replaced by polyandry (not done simultaneously ), Not sure whether the monogamous lines were maintained. This experiment was focussed on reproductive isolation anyway | |
| Gay, L., D. J. Hosken, P. Eady, R. Vasudev and T. Tregenza | 2011 | The Evolution of Harm-Effect of Sexual Conflicts and Population Size | Yes | Yes | No | No | 3 | Generations of monoandry were replaced by polyandry (not done simultaneously ), Not sure whether the monogamous lines were maintained. Also, did not directly look at SS+ vs SS- | |
| Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady | 2009 | Sperm competition and maternal effects differentially influence testis and sperm size in Callosobruchus maculatus | Yes | Yes | Yes | Yes | Yes | Appears to be direct comparison bw monogamous and polygamous structures | |
| Grazer, V. M., M. Demont, L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2014 | Environmental quality alters female costs and benefits of evolving under enforced monogamy | Yes | Yes | Yes | Yes | Yes | Direct Measures of fitness in environments that had standard and sub-standard food quality | |
| Grieshop, K., J. Stangberg, I. Martinossi-Allibert, G. Arnqvist and D. Berger | 2016 | Strong sexual selection in males against a mutation load that reduces offspring production in seed beetles | Yes | Yes | No | No | 3 | Different mating systems/ opportunity for SS were not imposed | |
| Hall, M. D., L. F. Bussiere and R. Brooks | 2009 | Diet-dependent female evolution influences male lifespan in a nuptial feeding insect | Yes | Yes | No | No | 3 | Different mating systems/ opportunity for SS were not imposed | |
| Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Experimental removal of sexual selection leads to decreased investment in an immune component in female Tribolium castaneum | Yes | Yes | Yes | Yes | Yes | ||
| Hangartner, S., S. H. Sbilordo, L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Are there genetic trade-offs between immune and reproductive investments in Tribolium castaneum? | Yes | Yes | Yes | Yes | Yes | Different levels of SS, but none with enforced monogamy (no choice) | |
| Hicks, S. K., K. L. Hagenbuch and L. M. Meffert | 2004 | Variable costs of mating, longevity, and starvation resistance in Musca domestica (Diptera: Muscidae) | Yes | Yes | No | No | 3 | Study on environmental conditions not SS treatment | |
| Holland, B. | 2002 | Sexual selection fails to promote adaptation to a new environment | Yes | Yes | Yes | Yes | Yes | Also looks at thermal stress | |
| Holland, B. and W. R. Rice | 1999 | Experimental removal of sexual selection reverses intersexual antagonistic coevolution and removes a reproductive load | Yes | Yes | Yes | Yes | Yes | ||
| Hollis, B., J. L. Fierst and D. Houle | 2009 | Sexual Selection Accelerates the Elimination of a Deleterious Mutant in Drosophila Melanogaster | Yes | Yes | Yes | Yes | Yes | looked at the purging of a deleterious allele | |
| Hollis, B. and D. Houle | 2011 | Populations with elevated mutation load do not benefit from the operation of sexual selection | Yes | Yes | Yes | Yes | Yes | Mutagenesis took place and direct fitness measurements were made | |
| Hollis, B., D. Houle and T. J. Kawecki | 2016 | Evolution of reduced post-copulatory molecular interactions in Drosophila populations lacking sperm competition | Yes | Yes | Yes | No | No | 4 | Seminal fluid proteins have a fitness advantage in a polygamous setting, thus is favoured; perhaps this was a bit too ambiguous. |
| Hollis, B., D. Houle, Z. Yan, T. J. Kawecki and L. Keller | 2014 | Evolution under monogamy feminizes gene expression in Drosophila melanogaster | Yes | Yes | Yes | No | No | 4 | Sex biased gene expression was measured, showing sexual antagonism. Would be stretched to consider it as a fitness measure. |
| Hollis, B. and T. J. Kawecki | 2014 | Male cognitive performance declines in the absence of sexual selection | Yes | Yes | Yes | Yes | Yes | Cognitive ability measured in both male and female | |
| Hollis, B., L. Keller and T. J. Kawecki | 2017 | Sexual selection shapes development and maturation rates in Drosophila | Yes | Yes | Yes | Yes | Yes | Development and fitness measured | |
| Hosken, D. J., O. Y. Martin, S. Wigby, T. Chapman and D. J. Hodgson | 2009 | Sexual conflict and reproductive isolation in flies | Yes | Yes | Yes | No | No | 4 | Reproductive isolation measured without fitness components |
| House, C. M., Z. Lewis, D. J. Hodgson, N. Wedell, M. D. Sharma, J. Hunt and D. J. Hosken | 2013 | Sexual and Natural Selection Both Influence Male Genital Evolution | Yes | Yes | Yes | No | No | 4 | Genitalia too complicated and hard to extract effect size |
| Hunt, J., R. R. Snook, C. Mitchell, H. S. Crudgington and A. J. Moore | 2012 | Sexual selection and experimental evolution of chemical signals in Drosophila pseudoobscura | Yes | Yes | Yes | No | No | 4 | Body size measured as well as CHC, like other studies may confer fitness advantage |
| Immonen, E., R. R. Snook and M. G. Ritchie | 2014 | Mating system variation drives rapid evolution of the female transcriptome in Drosophila pseudoobscura | Yes | Yes | Yes | Yes | Yes | While transcriptome outcomes not exclusively measuring fitness they also measures aspects of fecundity | |
| Innocenti, P., I. Flis and E. H. Morrow | 2014 | Female responses to experimental removal of sexual selection components in Drosophila melanogaster | Yes | Yes | Yes | Yes | Yes | To some extent the nature of SS treatment is unclear. Gene expression and fecundity are measured | |
| Jacomb, F., J. Marsh and L. Holman | 2016 | Sexual selection expedites the evolution of pesticide resistance | Yes | Yes | Yes | Yes | Yes | Pesticide Resistance as an environmental condition that needs to be adapted to | |
| Janicke, T., P. Sandner, S. A. Ramm, D. B. Vizoso and L. Schaerer | 2016 | Experimentally evolved and phenotypically plastic responses to enforced monogamy in a hermaphroditic flatworm | Yes | No | No | 2b | Hermaphroditic | ||
| Jarzebowska, M. and J. Radwan | 2010 | Sexual Selection Counteracts Extinction of Small Populations of the Bulb Mites | Yes | Yes | Yes | Yes | Yes | Direct fitness measurements over several generations | |
| Klemme, I. and R. C. Firman | 2013 | Male house mice that have evolved with sperm competition have increased mating duration and paternity success | Yes | Yes | Yes | Yes | Yes | Paternity Success measured | |
| Long, T. A. F., A. F. Agrawal and L. Rowe | 2012 | The Effect of Sexual Selection on Offspring Fitness Depends on the Nature of Genetic Variation | Yes | Yes | No | No | 3 | No enforced SS regimes | |
| Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Sexual selection protects against extinction | Yes | Yes | Yes | Yes | Yes | Reproductive fitness and time to extinction measured | |
| MacLellan, K., L. Kwan, M. C. Whitlock and H. D. Rundle | 2012 | Dietary stress does not strengthen selection against single deleterious mutations in Drosophila melanogaster | No | No | 1 | Selection based experiment rather than experimental evolution | |||
| MacLellan, K., M. C. Whitlock and H. D. Rundle | 2009 | Sexual selection against deleterious mutations via variable male search success | No | No | 1 | Selection based experiment rather than experimental evolution | |||
| Maklakov, A. A., R. Bonduriansky and R. C. Brooks | 2009 | Sex Differences, Sexual Selection, and Ageing: An Experimental Evolution Approach | Yes | Yes | Yes | Yes | Yes | Life History traits of ageing were measured | |
| Maklakov, A. A. and C. Fricke | 2009 | Sexual selection did not contribute to the evolution of male lifespan under curtailed age at reproduction in a seed beetle | Yes | Yes | Yes | No | No | 4 | Pseudoreplication to the above studies mut outcome metrics align less with the meta-analysis so we discard |
| Maklakov, A. A., C. Fricke and G. Arnqvist | 2007 | Sexual selection affects lifespan and aging in the seed beetle | Yes | Yes | Yes | No | No | 4 | Pseudoreplication to the above studies mut outcome metrics align less with the meta-analysis so we discard |
| Mallet, M. A., J. M. Bouchard, C. M. Kimber and A. K. Chippindale | 2011 | Experimental mutation-accumulation on the X chromosome of Drosophila melanogaster reveals stronger selection on males than females | Yes | Yes | No | No | 3 | No SS+ and SS- treatments | |
| Mallet, M. A. and A. K. Chippindale | 2011 | Inbreeding reveals stronger net selection on Drosophila melanogaster males: implications for mutation load and the fitness of sexual females | No | No | 1 | Mutation levels analysed | |||
| Martin, O. Y. and D. J. Hosken | 2003 | Costs and benefits of evolving under experimentally enforced polyandry or monogamy | Yes | Yes | Yes | Yes | Yes | Crossing took place after Gen 29, results still contain fitness components though | |
| Martin, O. Y. and D. J. Hosken | 2004 | Reproductive consequences of population divergence through sexual conflict | Yes | Yes | Yes | Yes | Yes | Crossing also took place, it should still be fine as they some populations were not crossed | |
| Matsuyama, T. and H. Kuba | 2009 | Mating time and call frequency of males between mass-reared and wild strains of melon fly, Bactrocera cucurbitae (Coquillett) (Diptera: Tephritidae) | Yes | Yes | No | No | 3 | Mate choice in different populations | |
| McGuigan, K., D. Petfield and M. W. Blows | 2011 | REDUCING MUTATION LOAD THROUGH SEXUAL SELECTION ON MALES | Yes | Yes | Yes | Yes | Yes | The control line was not enforced monomagous (did not remove SS)., it was just a control where the population was mutagenised. No clear SS treatment as level of selection varied across the generations. | |
| McKean, K. A. and L. Nunney | 2008 | Sexual selection and immune function in Drosophila melanogaster | Yes | Yes | Yes | Yes | Yes | The control line was a 1:1 SR but not enforced monogamy | |
| McLain, D. K. | 1992 | Population density and the intensity of sexual selection on body length in spatially or temporally restricted natural populations of a seed bug | No | No | 1 | Field study | |||
| McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Male-biased sex ratio does not promote increased sperm competitiveness in the seed beetle, Callosobruchus maculatus | Yes | Yes | Yes | Yes | Yes | No SS- (enforced monogamy) just altered SR | |
| McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | A test of the sexy-sperm and good-sperm hypotheses for the evolution of polyandry | Yes | Yes | Yes | Yes | Yes | Polygamy was still randomly done meaning post-cop SS is only available. Numorous measures of fitness conducted | |
| Meffert, L. M., J. L. Regan, S. K. Hicks, N. Mukana and S. B. Day | 2006 | Testing alternative methods for purging genetic load using the housefly (Musca domestica L.) | Yes | Yes | No | No | 3 | No tsts of SS | |
| Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson, T. Chapman and M. J. G. Gage | 2011 | Inbreeding Promotes Female Promiscuity | Yes | Yes | No | No | 3 | It does not appear the SS regimes were enforced (fig 1) | |
| Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Experimental Evolution Exposes Female and Male Responses to Sexual Selection and Conflict in Tribolium Castaneum | Yes | Yes | Yes | Yes | Yes | No enforced monogamy (no SS-), but different OSR | |
| Morrow, E. H., A. D. Stewart and W. R. Rice | 2008 | Assessing the extent of genome-wide intralocus sexual conflict via experimentally enforced gender-limited selection | Yes | Yes | No | No | 3 | Not using different SS treatment lines | |
| Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Sperm Competitive Ability Evolves in Response to Experimental Alteration of Operational Sex Ratio | Yes | Yes | Yes | Yes | Yes | Use an OSR of male and female bias | |
| Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Experimental Evolution of Female Traits under Different Levels of Intersexual Conflict in Drosophila Melanogaster | Yes | Yes | No | Yes | Yes | Use an OSR of male and female bias | |
| Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Rapid adaptation to mammalian sociality via sexually selected traits | Yes | Yes | Yes | Yes | Yes | 3 generations in mice with direct fitness outcomes | |
| Nie, H. and K. Kaneshiro | 2016 | Sexual selection and incipient speciation in Hawaiian Drosophila | No | No | 1 | Artificial selection was conducted alongside mate choice | |||
| Palopoli, M. F., C. Peden, C. Woo, K. Akiha, M. Ary, L. Cruze, J. L. Anderson and P. C. Phillips | 2015 | Natural and experimental evolution of sexual conflict within Caenorhabditis nematodes | Yes | No | No | 2b | Hermaphroditic, also competition not SS was modulated | ||
| Partridge, L. | 1980 | Mate Choice Increases a Component of Offspring Fitness in Fruit-Flies | Yes | Yes | Yes | Yes | Yes | Competitive success from 1 generation of populations with and without mate choice | |
| Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | The effects of sexual selection on life-history traits: An experimental study on guppies | Yes | Yes | Yes | Yes | Yes | Direct and indirect outcomes | |
| Perry, J. C., R. Joag, D. J. Hosken, N. Wedell, J. Radwan and S. Wigby | 2016 | Experimental evolution under hyper-promiscuity in Drosophila melanogaster | Yes | Yes | No | No | 3 | SS was manipulated with sex peptide receptor (SPR) not enforced selection conditions | |
| Pischedda, A. and A. Chippindale | 2005 | Sex, mutation and fitness: asymmetric costs and routes to recovery through compensatory evolution | No | No | 1 | Measures the effect of mutation in different populations | |||
| Pischedda, A. and A. K. Chippindale | 2006 | Intralocus sexual conflict diminishes the benefits of sexual selection | No | No | 1 | Focussed on fitness effects of conflict, not experimental evolution | |||
| Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Evolution of female remating behaviour following experimental removal of sexual selection | Yes | Yes | Yes | Yes | Yes | Body size and number of progeny measured. Not purpose of study though | |
| Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Males’ evolutionary responses to experimental removal of sexual selection | Yes | Yes | Yes | Yes | Yes | Male and population fitness outcomes measured | |
| Plesnar, A., M. Konior and J. Radwan | 2011 | The role of sexual selection in purging the genome of induced mutations in the bulb mite (Rizoglyphus robini) | Yes | Yes | Yes | Yes | Yes | ||
| Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop, M. Kolasa, M. Dzialo and J. Radwan | 2013 | No Evidence for Reproductive Isolation through Sexual Conflict in the Bulb Mite Rhizoglyphus robini | Yes | Yes | Yes | No | No | 4 | Reproductive isolation measured without fitness components |
| Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Mating system affects population performance and extinction risk under environmental challenge | Yes | Yes | Yes | Yes | Yes | ||
| Power, D. J. and L. Holman | 2014 | Polyandrous females found fitter populations | Yes | Yes | Yes | Yes | Yes | Remating was presented to the females 72 hours after first mating. Measuring effects of polyandry, thus multiple mating has more of an effect. Post copulatory selection will take place though. | |
| Power, D. J. and L. Holman | 2015 | Assessing the alignment of sexual and natural selection using radiomutagenized seed beetles | Yes | Yes | Yes | Yes | Yes | Experiment 2 Measures affect of SS | |
| Price, T. A. R., G. D. D. Hurst and N. Wedell | 2010 | Polyandry Prevents Extinction | Yes | Yes | No | No | 3 | Appears that individuals that only mated once still had a choice, post cop SS would be enacted then. Interested in mating freq over choice | |
| Prokop, Z. M., M. A. Prus, T. S. Gaczorek, K. Sychta, J. K. Palka, A. Plesnar-Bielak and M. Skarbon | 2017 | Do males pay for sex? Sex-specific selection coefficients suggest not | No | No | 1 | SS was estimated using models: not enforced in experimental evolution | |||
| Promislow, D. E. L., E. A. Smith and L. Pearse | 1998 | Adult fitness consequences of sexual selection in Drosophila melanogaster | Yes | Yes | Yes | Yes | Yes | ||
| Radwan, J. | 2004 | Effectiveness of sexual selection in removing mutations induced with ionizing radiation | Yes | Yes | Yes | Yes | Yes | Fitness outcomes measured | |
| Radwan, J., J. Unrug, K. Snigorska and K. Gawronska | 2004 | Effectiveness of sexual selection in preventing fitness deterioration in bulb mite populations under relaxed natural selection | Yes | Yes | Yes | Yes | Yes | Fitness outcomes measured | |
| Rundle, H. D., S. F. Chenoweth and M. W. Blows | 2006 | The roles of natural and sexual selection during adaptation to a novel environment | Yes | Yes | Yes | Yes | Yes | Fitness outcomes measured | |
| Rundle, H. D., S. F. Chenoweth and M. W. Blows | 2009 | The diversification of mate preferences by natural and sexual selection | Yes | Yes | Yes | No | No | 4 | CHCs / mate preference outcome measured alongside natural selection. CHCs not used in this meta-analysis |
| Rundle, H. D., A. Odeen and A. O. Mooers | 2007 | An experimental test for indirect benefits in Drosophila melanogaster | Yes | Yes | No | No | 3 | Between studs and duds not SS+ / SS- | |
| Savic Veselinovic, M., S. Pavkovic-Lucic, Z. Kurbalija Novicic, M. Jelic and M. Andelkovic | 2013 | Sexual Selection Can Reduce Mutational Load in Drosophila Subobscura | Yes | Yes | Yes | Yes | No | Data not suitable | Irradiated and direct fitness outcomes measured: However when extracting data there were no sample sizes presented so we excluded the study as author did not respond to email |
| Seslija, D., I. Marecko and N. Tucic | 2008 | Sexual selection and senescence: Do seed beetle males (Acanthoscelides obtectus, Bruchidae, Coleoptera) shape the longevity of their mates? | Yes | Yes | No | No | 3 | While there is monoandrous lines, these lines were not enforced and choice still existed. Put post-cop choice may be stronger in other lines. This is a strange setup and may be hard to compare with other studies | |
| Sharma, M. D., J. Hunt and D. J. Hosken | 2012 | Antagonistic Responses to Natural and Sexual Selection and the Sex-Specific Evolution of Cuticular Hydrocarbons in Drosophila Simulans | Yes | Yes | Yes | No | No | 4 | CHCs / mate preference outcome measured alongside natural selection |
| Sharp, N. P. and A. F. Agrawal | 2008 | Mating density and the strength of sexual selection against deleterious alleles in Drosophila melanogaster | No | No | 1 | One generation w/ gene freq. Also no enforced monogamy | |||
| Sharp, N. P. and A. F. Agrawal | 2009 | Sexual Selection and the Random Union of Gametes: Testing for a Correlation in Fitness between Mates in Drosophila melanogaster | No | No | 1 | Assortive mating study | |||
| Simmons, L. W. and R. C. Firman | 2014 | Experimental Evidence for the Evolution of the Mammalian Baculum by Sexual Selection | Yes | Yes | Yes | No | No | 4 | States that “Far less is known of the fitness consequences of variation in baculum morphology for mammals.” - No direct link with fitness advantage. Genital morphology not used in this meta-analysis |
| Simmons, L. W. and F. Garcia-Gonzalez | 2008 | Evolutionary Reduction in Testes Size and Competitive Fertilization Success in Response to the Experimental Removal of Sexual Selection in Dung Beetles | Yes | Yes | Yes | Yes | Yes | Fitness outcomes measured | |
| Simmons, L. W. and F. Garcia-Gonzalez | 2011 | Experimental coevolution of male and female genital morphology | Yes | Yes | Yes | No | No | 4 | Genital morphology has conflicting fitness outcomes for males and females, not used in this meta-analysis |
| Simmons, L. W., C. M. House, J. Hunt and F. Garcia-Gonzalez | 2009 | Evolutionary Response to Sexual Selection in Male Genital Morphology | Yes | Yes | Yes | No | No | 4 | Genital Morphology not used in this meta-analysis |
| Snook, R. R., N. A. Gidaszewski, T. Chapman and L. W. Simmons | 2013 | Sexual selection and the evolution of secondary sexual traits: sex comb evolution in Drosophila | Yes | Yes | Yes | No | No | 4 | In D. pseudo monogamy was enforced. Sex combs are cited as having positive fitness effects at high and low numbers. Would not give an accurate representation of a fitness comparison |
| Tilszer, M., K. Antoszczyk, N. Salek, E. Zajac and J. Radwan | 2006 | Evolution under relaxed sexual conflict in the bulb mite Rhizoglyphus robini | Yes | Yes | Yes | Yes | Yes | ||
| van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Rapid Loss of Behavioral Plasticity and Immunocompetence under Intense Sexual Selection | Yes | Yes | Yes | Yes | Yes | Did not use enforced monogamy but had different operational sex ratio | |
| Whitlock, M. C. and D. Bourguet | 2000 | Factors affecting the genetic load in Drosophila: Synergistic epistasis and correlations among fitness components | Yes | Yes | No | No | 3 | No manipulation of sexual selection | |
| Wigby, S. and T. Chapman | 2004 | Female resistance to male harm evolves in response to manipulation of sexual conflict | Yes | Yes | Yes | Yes | Yes | Did not use enforced monogamy but had different sex ratio |
Data Extraction and Effect Size Calculation
The rules utilised during the data extraction and effect size calculation were as follows:
Arithmatic means, standard deviations/errors and sample sizes were extracted from a paper, supplementary material or a linked data repository (e.g. Data Dryad). This was possible when means and SD were reported in text or in a table. We would preferentially extract data for each experimental evolution line/replicat/family if possible and only extract data for the final reported generation (which was noted down).
If we could not find the means and SD in text format we used web-plot digitizer (v.3.12) to extract data from graphs.
If means were not reported then we extracted a summary statistic or proportion value, which we could later convert to Hedges g’ using the compute.es package (Re 2013). Summary statistics included F, z, t and chi2. These conversions still required providing sample sizes for each treatment so these needed to be extractable from the study. Some summary statistics were obtained from generalized linear model summary tabels, others from straight forward ANOVAs and then some from more complex analysis such as proportional hazards statistical tests.
We also collected various covariates for some of the studies (Table S3), which are discussed later.
The Effect Size Dataset
Table of Effect Sizes
Table S3: Table of effect sizes included in our meta-analysis. See the text following the table for an explanation of each column.
# Load the data and clean up the variable formats
prelim.data <- read.csv('data/Preliminary data frame 22.2.18.csv')
prelim.data$Study.ID <- prelim.data$Study.ID %>% factor()
prelim.data$Group.ID <- prelim.data$Group.ID %>% factor()
prelim.data$Environment <- prelim.data$Environment %>% relevel(ref="Unstressed")
prelim.data$Sex <- prelim.data$Sex %>% relevel(ref="B")
#Outcome.Class.2 is using the categories that were decided by survey. I am keeping both just to check them against each other (how much of a difference it makes)
prelim.data$Outcome.Class <- prelim.data$Outcome.Class %>% relevel(ref="Indirect")
prelim.data$Enforced.Monogamy <- prelim.data$Enforced.Monogamy %>% relevel(ref="NO")
prelim.data$Pre.cop <- prelim.data$Pre.cop %>% factor() %>% relevel(ref="0")
prelim.data$Post.cop <- prelim.data$Post.cop %>% factor() %>% relevel(ref="0")
prelim.data$Blinding <- prelim.data$Blinding %>% factor()
kable(prelim.data, "html") %>%
kable_styling() %>%
scroll_box(width = "100%", height = "500px")| Study.ID | Group.ID | Authors | Year | AuthorYear | Species | Taxon | SS.density.high.to.low | SS.ratio.high | SS.density.high | Pre.cop | Post.cop | Blinding | Generations | Enforced.Monogamy | n | Outcome | Sex | Ambiguous | Outcome.Class | Environment | g | var.g | Positive.Fitness | mean.low | sd.low | n.low | mean.high | sd.high | n.high | JIF |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 37 | Almbro, M. and L. W. Simmons | 2014 | Almbro 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 3 | YES | 182 | Strength | M | NO | Indirect | Stressed | 0.385 | 0.022 | 1 | 0.0470000 | 0.0572364 | 91 | 0.0940000 | 0.1621697 | 91 | 4.612 |
| 1 | 37 | Almbro, M. and L. W. Simmons | 2014 | Almbro 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 3 | YES | 182 | Strength | M | NO | Indirect | Unstressed | 0.000 | 0.022 | 1 | 0.1170000 | 0.1717091 | 91 | 0.1170000 | 0.1717091 | 91 | 4.612 |
| 1 | 37 | Almbro, M. and L. W. Simmons | 2014 | Almbro 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 3 | YES | 222 | Ejaculate Quality and Production | M | NO | Indirect | Stressed | 0.172 | 0.018 | 1 | 1.8920000 | 0.9060662 | 111 | 2.0510000 | 0.9376732 | 111 | 4.612 |
| 1 | 37 | Almbro, M. and L. W. Simmons | 2014 | Almbro 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 3 | YES | 222 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.204 | 0.018 | 1 | 2.1900000 | 0.9692801 | 111 | 2.3820000 | 0.9060662 | 111 | 4.612 |
| 1 | 37 | Almbro, M. and L. W. Simmons | 2014 | Almbro 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 2 | YES | 414 | Reproductive Success | F | NO | Direct | Not Stated | 0.258 | 0.010 | 1 | 15.4000000 | 10.0712462 | 207 | 18.0000000 | 10.0712462 | 207 | 4.612 |
| 2 | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Arbuthnott 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 7 | YES | 400 | Mutant Frequency | B | NO | Indirect | Stressed | -0.011 | 0.010 | -1 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Arbuthnott 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 7 | YES | 400 | Mutant Frequency | B | NO | Indirect | Stressed | 0.434 | 0.010 | -1 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Arbuthnott 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 7 | YES | 400 | Mutant Frequency | B | NO | Indirect | Stressed | -0.064 | 0.010 | -1 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Arbuthnott 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 7 | YES | 400 | Mutant Frequency | B | NO | Indirect | Stressed | -0.037 | 0.010 | -1 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Arbuthnott 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 7 | YES | 400 | Mutant Frequency | B | NO | Indirect | Stressed | -0.129 | 0.010 | -1 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Arbuthnott 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 7 | YES | 400 | Mutant Frequency | B | NO | Indirect | Stressed | 0.032 | 0.010 | -1 | NA | NA | NA | NA | NA | NA | 4.864 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Archer 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | Lifespan | M | NO | Indirect | Stressed | -0.971 | 0.005 | 1 | 30.5200000 | 5.9396970 | 450 | 24.2100000 | 7.0003571 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Archer 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | Lifespan | F | NO | Indirect | Stressed | -0.154 | 0.004 | 1 | 34.2800000 | 26.9407684 | 450 | 31.3200000 | 4.0305087 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Archer 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | Fitness Senescence | M | NO | Indirect | Stressed | 0.074 | 0.004 | -1 | 3.6300000 | 1.2727922 | 450 | 3.4300000 | 3.6062446 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Archer 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | Fitness Senescence | F | NO | Indirect | Stressed | -0.087 | 0.004 | -1 | 3.9200000 | 1.4849242 | 450 | 4.3500000 | 6.7882251 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Archer 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | Offspring Viability | M | NO | Direct | Stressed | -0.868 | 0.005 | -1 | 0.0295858 | 0.0063640 | 450 | 0.0372000 | 0.0106066 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Archer 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | Offspring Viability | F | NO | Direct | Stressed | -0.148 | 0.004 | -1 | 0.0264000 | 0.0254558 | 450 | 0.0291000 | 0.0042426 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Archer 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | Lifespan | M | NO | Indirect | Unstressed | -0.780 | 0.005 | 1 | 35.5500000 | 11.0308658 | 450 | 26.9400000 | 11.0308658 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Archer 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | Lifespan | F | NO | Indirect | Unstressed | -0.146 | 0.004 | 1 | 34.2800000 | 26.9407684 | 450 | 30.1900000 | 28.8499567 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Archer 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | Fitness Senescence | M | NO | Indirect | Unstressed | 0.021 | 0.004 | -1 | 4.5000000 | 6.1518290 | 450 | 4.3300000 | 9.9702056 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Archer 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | Fitness Senescence | F | NO | Indirect | Unstressed | -0.038 | 0.004 | -1 | 4.8200000 | 5.9396970 | 450 | 5.1500000 | 10.8187337 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Archer 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | Offspring Viability | M | NO | Direct | Unstressed | -0.855 | 0.005 | -1 | 0.2580000 | 0.0042426 | 450 | 0.0339000 | 0.0127279 | 450 | 5.210 |
| 3 | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Archer 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | Offspring Viability | F | NO | Direct | Unstressed | -0.176 | 0.004 | -1 | 0.0267000 | 0.0169706 | 450 | 0.0305000 | 0.0254558 | 450 | 5.210 |
| 5 | 1 | Bernasconi, G. and L. Keller | 2001 | Bernasconi 2001 | Tribolium castaneum | Beetle | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 3 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | 1.533 | 0.242 | 1 | 0.5600000 | 0.3600000 | 10 | 0.9700000 | 0.0400000 | 10 | 2.673 |
| 5 | 1 | Bernasconi, G. and L. Keller | 2001 | Bernasconi 2001 | Tribolium castaneum | Beetle | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 3 | YES | 20 | Reproductive Success | F | NO | Direct | Unstressed | -0.123 | 0.184 | 1 | 63.0000000 | 27.0000000 | 10 | 60.0000000 | 19.0000000 | 10 | 2.673 |
| 6 | 15 | Brommer, J. E., C. Fricke, D. A. Edward and T. Chapman | 2012 | Brommer 2012 | Drosophila melanogaster | Fly | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 4 | YES | 93 | Reproductive Success | B | NO | Direct | Unstressed | -0.378 | 0.043 | 1 | 1.0000000 | 0.3316625 | 44 | 0.8700000 | 0.3500000 | 49 | 4.864 |
| 7 | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Crudgington 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 21 | YES | 200 | Reproductive Success | F | NO | Direct | Stressed | -0.216 | 0.020 | 1 | 76.9000000 | 47.0000000 | 100 | 66.4000000 | 50.0000000 | 100 | 4.464 |
| 7 | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Crudgington 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 28 | YES | 200 | Reproductive Success | F | NO | Direct | Stressed | 0.280 | 0.020 | 1 | 120.6000000 | 119.0000000 | 100 | 153.6000000 | 116.0000000 | 100 | 4.464 |
| 7 | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Crudgington 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 28 | YES | 200 | Offspring Viability | F | NO | Direct | Stressed | 0.365 | 0.045 | 1 | 0.7810000 | 0.0700000 | 100 | 0.8740000 | 0.0500000 | 100 | 4.464 |
| 7 | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Crudgington 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 21 | YES | 200 | Reproductive Success | F | NO | Direct | Unstressed | -0.244 | 0.020 | 1 | 108.5000000 | 44.0000000 | 100 | 97.9000000 | 43.0000000 | 100 | 4.464 |
| 7 | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Crudgington 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 28 | YES | 200 | Reproductive Success | F | NO | Direct | Unstressed | 0.281 | 0.020 | 1 | 164.1000000 | 119.0000000 | 100 | 197.5000000 | 119.0000000 | 100 | 4.464 |
| 7 | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Crudgington 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 28 | YES | 200 | Offspring Viability | F | NO | Direct | Unstressed | -0.311 | 0.155 | 1 | 0.9680000 | 0.0400000 | 100 | 0.9450000 | 0.0400000 | 100 | 4.464 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 62 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | -0.168 | 0.184 | 1 | 15.7249071 | 1.9984654 | 10 | 15.3903346 | 1.7633519 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 61 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | -0.576 | 0.192 | 1 | 15.3903346 | 2.1160222 | 10 | 14.3122677 | 1.4106815 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 60 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.311 | 0.226 | 1 | 15.0185874 | 1.0580111 | 10 | 16.3940520 | 0.9404543 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 58 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 0.512 | 0.190 | 1 | 15.7992565 | 1.6457951 | 10 | 16.6542751 | 1.5282383 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 62 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.373 | 0.231 | 1 | 15.7249071 | 1.9984654 | 10 | 18.0669145 | 1.1755679 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 61 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.190 | 0.219 | 1 | 15.3903346 | 2.1160222 | 10 | 17.6208178 | 1.4106815 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 60 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.305 | 0.226 | 1 | 15.0185874 | 1.0580111 | 10 | 17.1003718 | 1.8809086 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 58 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.928 | 0.276 | 1 | 15.7992565 | 1.6457951 | 10 | 18.5873606 | 1.0580111 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 62 | NO | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.713 | 0.257 | 1 | 15.3903346 | 1.7633519 | 10 | 18.0700000 | 1.1755679 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 61 | NO | 10 | Mating Success | M | NO | Indirect | Unstressed | 2.248 | 0.310 | 1 | 14.3122677 | 1.4106815 | 10 | 17.6200000 | 1.4106815 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 60 | NO | 10 | Mating Success | M | NO | Indirect | Unstressed | 0.458 | 0.189 | 1 | 16.3940520 | 0.9404543 | 10 | 17.1000000 | 1.8809086 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 58 | NO | 10 | Mating Success | M | NO | Indirect | Unstressed | 1.414 | 0.233 | 1 | 16.6542751 | 1.5282383 | 10 | 18.5900000 | 1.0580111 | 10 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 60 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.060 | 0.096 | 1 | 622.3853211 | 367.6177813 | 20 | 642.9357798 | 301.9717489 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 59 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | -0.089 | 0.096 | 1 | 760.3669725 | 407.0054007 | 20 | 733.9449541 | 354.4885748 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 57 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | -0.214 | 0.097 | 1 | 728.0733945 | 407.0054007 | 20 | 648.8073394 | 315.1009554 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 60 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.515 | 0.099 | 1 | 622.4000000 | 367.6177800 | 20 | 819.0825688 | 380.7469877 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 59 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.768 | 0.103 | 1 | 760.4000000 | 407.0054000 | 20 | 1200.7339450 | 682.7187366 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 57 | YES | 20 | Reproductive Success | M | NO | Direct | Unstressed | 1.068 | 0.110 | 1 | 728.1000000 | 407.0054000 | 20 | 1150.8256880 | 367.6177813 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 60 | NO | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.503 | 0.099 | 1 | 642.9357798 | 301.9717489 | 20 | 819.0825688 | 380.7469877 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 59 | NO | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.841 | 0.105 | 1 | 733.9449541 | 354.4885748 | 20 | 1200.7339450 | 682.7187366 | 20 | 5.429 |
| 8 | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Crudgington 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 57 | NO | 20 | Reproductive Success | M | NO | Direct | Unstressed | 1.437 | 0.122 | 1 | 648.8073394 | 315.1009554 | 20 | 1150.8256880 | 367.6177813 | 20 | 5.429 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 55 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | -0.861 | 0.111 | 1 | 237.3000000 | 55.0072700 | 20 | 169.5000000 | 95.8836795 | 18 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 54 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | -0.655 | 0.118 | 1 | 210.6000000 | 67.6189300 | 17 | 170.5000000 | 50.7141992 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 55 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.026 | 0.123 | 1 | 0.5230000 | 0.1833600 | 20 | 0.5350000 | 0.2460732 | 18 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 54 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.360 | 0.140 | 1 | 0.4960000 | 0.1772900 | 17 | 0.6590000 | 0.2680019 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 55 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | -1.447 | 0.132 | 1 | 237.3000000 | 55.0072700 | 20 | 150.0000000 | 63.4958266 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 54 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | -0.739 | 0.114 | 1 | 210.6000000 | 67.6189300 | 17 | 154.1000000 | 80.6396305 | 19 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 55 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.620 | 0.160 | 1 | 0.5230000 | 0.1833600 | 20 | 0.7750000 | 0.2185246 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 54 | YES | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.450 | 0.140 | 1 | 0.4960000 | 0.1772900 | 17 | 0.6930000 | 0.2310216 | 19 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 55 | NO | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | -0.233 | 0.110 | 1 | 169.5000000 | 95.8836795 | 18 | 150.0000000 | 63.4958266 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 54 | NO | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | -0.235 | 0.107 | 1 | 170.5000000 | 50.7141992 | 17 | 154.1000000 | 80.6396305 | 19 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 55 | NO | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.590 | 0.160 | 1 | 0.5350000 | 0.2460732 | 18 | 0.7750000 | 0.2185246 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 54 | NO | 18 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.080 | 0.150 | 1 | 0.6590000 | 0.2680019 | 17 | 0.6930000 | 0.2310216 | 19 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 55 | YES | 18 | Reproductive Success | F | NO | Direct | Unstressed | -0.520 | 0.105 | 1 | 500.3000000 | 174.4133000 | 20 | 403.8000000 | 261.3466663 | 18 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 54 | YES | 18 | Reproductive Success | F | NO | Direct | Unstressed | -0.843 | 0.123 | 1 | 474.7000000 | 195.0229000 | 17 | 315.4000000 | 173.5827468 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 55 | YES | 18 | Reproductive Success | F | NO | Direct | Unstressed | -2.487 | 0.188 | 1 | 403.8000000 | 261.3466663 | 18 | 228.1000000 | 152.5549081 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 54 | YES | 18 | Reproductive Success | F | NO | Direct | Unstressed | -1.065 | 0.122 | 1 | 315.4000000 | 173.5827468 | 17 | 266.1000000 | 188.3044344 | 19 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 55 | NO | 18 | Reproductive Success | F | NO | Direct | Unstressed | -0.796 | 0.118 | 1 | 500.3000000 | 174.4133000 | 20 | 228.1000000 | 152.5549081 | 17 | 3.636 |
| 9 | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Crudgington 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 54 | NO | 18 | Reproductive Success | F | NO | Direct | Unstressed | -0.266 | 0.108 | 1 | 474.7000000 | 195.0229000 | 17 | 266.1000000 | 188.3044344 | 19 | 3.636 |
| 10 | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Debelle 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 98 | YES | 2038 | Body Size | M | YES | Ambiguous | Unstressed | 0.555 | 0.002 | 1 | 2.2200000 | 0.0730000 | 1019 | 2.2600000 | 0.0710000 | 1019 | 2.792 |
| 10 | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Debelle 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 98 | YES | 2038 | Body Size | F | YES | Ambiguous | Unstressed | 0.111 | 0.002 | 1 | 2.4500000 | 0.0820000 | 1019 | 2.4600000 | 0.0980000 | 1019 | 2.792 |
| 10 | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Debelle 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 98 | YES | 2038 | Mating Success | M | NO | Indirect | Unstressed | -0.663 | 0.004 | 1 | NA | NA | NA | NA | NA | NA | 2.792 |
| 10 | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Debelle 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 98 | YES | 2038 | Mating Success | M | NO | Indirect | Unstressed | -0.655 | 0.004 | 1 | NA | NA | NA | NA | NA | NA | 2.792 |
| 10 | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Debelle 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 98 | YES | 2038 | Mating Latency | M | YES | Indirect | Unstressed | -0.197 | 0.002 | -1 | 126.5000000 | 15.8000000 | 1019 | 129.4000000 | 13.5000000 | 1019 | 2.792 |
| 10 | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Debelle 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 98 | YES | 2038 | Mating Latency | M | YES | Indirect | Unstressed | 2.486 | 0.003 | -1 | 153.8000000 | 19.7000000 | 1019 | 113.6000000 | 11.6000000 | 1019 | 2.792 |
| 11 | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Demont 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 36 | YES | 38 | Reproductive Success | F | NO | Direct | Stressed | 1.810 | 0.144 | 1 | 91.7000000 | 9.4400000 | 19 | 105.7700000 | 5.1700000 | 19 | 2.606 |
| 11 | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Demont 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 36 | YES | 38 | Reproductive Success | F | NO | Direct | Unstressed | 0.299 | 0.102 | 1 | 93.9700000 | 21.3500000 | 19 | 101.2400000 | 26.0600000 | 19 | 2.606 |
| 11 | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Demont 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 36 | YES | 24 | Reproductive Success | M | NO | Direct | Unstressed | 0.222 | 0.156 | 1 | 106.8500000 | 6.2000000 | 12 | 108.6500000 | 9.2000000 | 12 | 2.606 |
| 11 | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Demont 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 36 | YES | 24 | Reproductive Success | M | NO | Direct | Unstressed | 0.279 | 0.209 | 1 | 0.3000000 | 0.0500000 | 12 | 0.4200000 | 0.0500000 | 12 | 2.606 |
| 11 | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Demont 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 36 | YES | 44 | Offspring Viability | F | NO | Direct | Unstressed | 0.415 | 0.090 | 1 | 24.0000000 | 8.9442719 | 20 | 27.0000000 | 4.8989795 | 24 | 2.606 |
| 11 | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Demont 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 36 | YES | 45 | Offspring Viability | M | NO | Direct | Unstressed | 0.407 | 0.088 | 1 | 23.0000000 | 9.3808315 | 22 | 26.0000000 | 4.7958315 | 23 | 2.606 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Edward 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | Mating Latency | M | NO | Indirect | Stressed | -0.324 | 0.020 | 1 | 6.5230000 | 5.4190000 | 102 | 5.0170000 | 3.6600000 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Edward 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | Mating Duration | M | NO | Ambiguous | Stressed | 0.219 | 0.020 | 1 | 11.9500000 | 2.9810000 | 102 | 12.6440000 | 3.3310000 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Edward 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | Mating Latency | M | NO | Indirect | Unstressed | 0.099 | 0.019 | 1 | 5.5121951 | 3.5893711 | 102 | 5.8885017 | 3.9412702 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Edward 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | Mating Duration | M | NO | Ambiguous | Unstressed | 0.393 | 0.020 | 1 | 9.1892361 | 2.5424101 | 102 | 10.4565972 | 3.7697805 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Edward 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | Reproductive Success | F | NO | Direct | Stressed | 0.070 | 0.019 | 1 | 72.3810000 | 35.0550000 | 102 | 74.8857645 | 35.9428779 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Edward 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | Reproductive Success | F | NO | Direct | Stressed | 0.015 | 0.019 | 1 | 0.6410000 | 0.5090000 | 102 | 0.6491071 | 0.5545710 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Edward 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | Reproductive Success | M | NO | Direct | Stressed | 0.001 | 0.019 | 1 | 0.7750000 | 0.6600000 | 102 | 0.7759516 | 0.7391160 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Edward 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | Reproductive Success | F | NO | Direct | Unstressed | -0.312 | 0.020 | 1 | 81.9595782 | 34.6116602 | 102 | 71.0632689 | 35.0553994 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Edward 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | Reproductive Success | F | NO | Direct | Unstressed | 0.099 | 0.019 | 1 | 0.5946429 | 0.5004665 | 102 | 0.6446429 | 0.5049752 | 102 | 8.090 |
| 12 | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Edward 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | Reproductive Success | M | NO | Direct | Unstressed | 0.049 | 0.019 | 1 | 0.7157439 | 0.6919384 | 102 | 0.7510381 | 0.7495999 | 102 | 8.090 |
| 13 | 6 | Firman, R. C. | 2011 | Firman 2011a | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | F | NO | Direct | Stressed | 0.396 | 0.080 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Firman 2011a | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | F | NO | Direct | Stressed | -1.258 | 0.114 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Firman 2011a | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | F | NO | Direct | Stressed | -0.352 | 0.076 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Firman 2011a | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | F | NO | Direct | Stressed | 1.316 | 0.146 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Firman 2011a | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | M | NO | Direct | Stressed | 1.196 | 0.132 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Firman 2011a | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | M | NO | Direct | Stressed | 1.142 | 0.104 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Firman 2011a | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | M | NO | Direct | Stressed | 0.131 | 0.072 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman, R. C. | 2011 | Firman 2011a | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | Reproductive Success | M | NO | Direct | Stressed | 1.747 | 0.360 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 14 | 6 | Firman, R. C. | 2014 | Firman 2011a | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 25 | YES | 30 | Male Attractiveness | M | NO | Indirect | Unstressed | -1.177 | 0.149 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 15 | 6 | Firman, R. C., L. Y. Cheam and L. W. Simmons | 2011 | Firman 2011b | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 8 | YES | 54 | Ejaculate Quality and Production | M | NO | Indirect | Not Stated | 0.303 | 0.073 | 1 | NA | NA | NA | NA | NA | NA | 3.276 |
| 15 | 6 | Firman, R. C., L. Y. Cheam and L. W. Simmons | 2011 | Firman 2011b | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 8 | YES | 54 | Ejaculate Quality and Production | M | NO | Indirect | Not Stated | 1.844 | 0.105 | 1 | NA | NA | NA | NA | NA | NA | 3.276 |
| 16 | 6 | Firman, R. C., F. Garcia-Gonzalez, E. Thyer, S. Wheeler, Z. Yamin, M. Yuan and L. W. Simmons | 2015 | Firman 2015 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 18 | YES | 60 | Ejaculate Quality and Production | M | NO | Indirect | Not Stated | 1.003 | 0.073 | 1 | 0.7010000 | 0.0492950 | 30 | 0.7470000 | 0.0438178 | 30 | 4.007 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Firman 2014b | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 88 | Reproductive Success | F | NO | Direct | Not Stated | -0.963 | 0.068 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Firman 2014b | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 41 | Reproductive Success | F | NO | Direct | Not Stated | -1.733 | 0.349 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Firman 2014b | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 78 | Reproductive Success | F | NO | Direct | Not Stated | -1.717 | 0.111 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Firman 2014b | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 55 | Reproductive Success | F | NO | Direct | Not Stated | -0.974 | 0.115 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Firman 2014b | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 86 | Reproductive Success | F | NO | Direct | Not Stated | -0.599 | 0.102 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Firman 2014b | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 55 | Reproductive Success | F | NO | Direct | Not Stated | -0.904 | 0.159 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Firman 2014b | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 36 | Reproductive Success | F | NO | Direct | Not Stated | -0.504 | 0.199 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 18 | 6 | Firman, R. C. and L. W. Simmons | 2010 | Firman 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 8 | YES | 144 | Ejaculate Quality and Production | M | NO | Indirect | Not Stated | 0.399 | 0.026 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 18 | 6 | Firman, R. C. and L. W. Simmons | 2010 | Firman 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 7 | YES | 40 | Reproductive Success | F | NO | Direct | Stressed | -0.564 | 0.100 | 1 | 17.5500000 | 5.4112845 | 20 | 14.4500000 | 5.3665631 | 20 | 3.521 |
| 18 | 6 | Firman, R. C. and L. W. Simmons | 2010 | Firman 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 7 | YES | 40 | Reproductive Success | F | NO | Direct | Unstressed | -0.328 | 0.097 | 1 | 16.1500000 | 4.1143651 | 20 | 14.5500000 | 5.3665631 | 20 | 3.521 |
| 18 | 6 | Firman, R. C. and L. W. Simmons | 2010 | Firman 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 10 | YES | 144 | Reproductive Success | F | NO | Direct | Not Stated | 0.668 | 0.029 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 18 | 6 | Firman, R. C. and L. W. Simmons | 2010 | Firman 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 8 | YES | 128 | Body Size | B | NO | Ambiguous | Not Stated | -0.364 | 0.031 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 19 | 6 | Firman, R. C. and L. W. Simmons | 2011 | Firman 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 12 | YES | 128 | Reproductive Success | M | NO | Direct | Stressed | -1.008 | 0.035 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 20 | 6 | Firman, R. C. and L. W. Simmons | 2012 | Firman 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 15 | YES | 144 | Reproductive Success | F | NO | Direct | Unstressed | 0.784 | 0.030 | 1 | 4.9400000 | 2.2910260 | 72 | 6.6500000 | 2.0364675 | 72 | 3.521 |
| 20 | 6 | Firman, R. C. and L. W. Simmons | 2012 | Firman 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 15 | YES | 128 | Reproductive Success | F | NO | Direct | Unstressed | -0.213 | 0.031 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 20 | 6 | Firman, R. C. and L. W. Simmons | 2012 | Firman 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 15 | YES | 128 | Reproductive Success | F | NO | Direct | Unstressed | 0.416 | 0.032 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 20 | 6 | Firman, R. C. and L. W. Simmons | 2012 | Firman 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 15 | YES | 128 | Offspring Viability | B | NO | Direct | Unstressed | 0.014 | 0.031 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 20 | 6 | Firman, R. C. and L. W. Simmons | 2012 | Firman 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 15 | YES | 128 | Offspring Viability | B | NO | Direct | Unstressed | 0.408 | 0.032 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Fricke 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 155 | Body Size | F | YES | Ambiguous | Not Stated | 0.080 | 0.026 | 1 | 0.0011635 | 0.0001053 | 77 | 0.0011720 | 0.0001073 | 78 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Fricke 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 155 | Body Size | M | YES | Ambiguous | Not Stated | 0.102 | 0.026 | 1 | 0.0009178 | 0.0000963 | 77 | 0.0009283 | 0.0001084 | 77 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Fricke 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 76 | Development Rate | B | NO | Ambiguous | Stressed | -0.453 | 0.053 | 1 | 0.8289099 | 0.0286151 | 38 | 0.8135570 | 0.0377825 | 38 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Fricke 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 79 | Development Rate | B | NO | Ambiguous | Unstressed | 0.772 | 0.053 | 1 | 0.8251609 | 0.0378719 | 39 | 0.8534363 | 0.0346177 | 40 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Fricke 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 76 | Reproductive Success | F | NO | Direct | Stressed | -0.579 | 0.054 | 1 | 419.3947368 | 34.3546995 | 38 | 397.4210526 | 40.5573545 | 38 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Fricke 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 79 | Reproductive Success | F | NO | Direct | Unstressed | 0.185 | 0.050 | 1 | 292.2051282 | 55.6900159 | 39 | 301.0500000 | 37.3678526 | 40 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Fricke 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 76 | Offspring Viability | F | NO | Direct | Stressed | -0.476 | 0.053 | 1 | 0.5462428 | 0.0780889 | 38 | 0.5107408 | 0.0691831 | 38 | 4.502 |
| 22 | 9 | Fricke, C. and G. Arnqvist | 2007 | Fricke 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 79 | Offspring Viability | F | NO | Direct | Unstressed | 0.543 | 0.052 | 1 | 0.4180605 | 0.0784570 | 39 | 0.4575765 | 0.0652579 | 40 | 4.502 |
| 23 | 7 | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Fritzsche 2016 | Megabruchidius dorsalis | Beetle | 1.000 | 5.00 | 150.00 | 1 | 1 | Blind | 20 | NO | 1200 | Reproductive Success | M | NO | Direct | Not Stated | -0.056 | 0.003 | 1 | NA | NA | NA | NA | NA | NA | 8.851 |
| 23 | 7 | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Fritzsche 2016 | Megabruchidius dorsalis | Beetle | 1.000 | 5.00 | 150.00 | 1 | 1 | Blind | 20 | NO | 1200 | Reproductive Success | F | NO | Direct | Not Stated | -0.031 | 0.003 | 1 | NA | NA | NA | NA | NA | NA | 8.851 |
| 23 | 7 | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Fritzsche 2016 | Megabruchidius dorsalis | Beetle | 1.000 | 5.00 | 150.00 | 1 | 1 | Blind | 20 | NO | 1200 | Lifespan | M | NO | Indirect | Not Stated | -0.066 | 0.003 | 1 | NA | NA | NA | NA | NA | NA | 8.851 |
| 23 | 7 | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Fritzsche 2016 | Megabruchidius dorsalis | Beetle | 1.000 | 5.00 | 150.00 | 1 | 1 | Blind | 20 | NO | 1200 | Lifespan | F | NO | Indirect | Not Stated | -0.083 | 0.003 | 1 | NA | NA | NA | NA | NA | NA | 8.851 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Fritzsche 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 90 | Ejaculate Quality and Production | M | NO | Indirect | Not Stated | -0.197 | 0.044 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Fritzsche 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 256 | Mating Success | M | NO | Indirect | Not Stated | -0.041 | 0.016 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Fritzsche 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 256 | Mating Success | M | NO | Indirect | Not Stated | -0.065 | 0.016 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Fritzsche 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 256 | Mating Success | M | NO | Indirect | Not Stated | -0.078 | 0.016 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Fritzsche 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 256 | Mating Success | M | NO | Indirect | Not Stated | -0.267 | 0.016 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Fritzsche 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 184 | Reproductive Success | B | NO | Direct | Not Stated | 0.095 | 0.022 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Fritzsche 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 184 | Reproductive Success | B | NO | Direct | Not Stated | 0.407 | 0.022 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Fritzsche 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 392 | Reproductive Success | B | NO | Direct | Not Stated | 0.059 | 0.010 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Fritzsche 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 392 | Reproductive Success | B | NO | Direct | Not Stated | 0.219 | 0.010 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 26 | 10 | Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady | 2009 | Gay 2009 | Callosobruchus maculatus | Beetle | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 90 | YES | 80 | Body Size | M | YES | Ambiguous | Unstressed | 1.971 | 0.073 | 1 | 1.8700000 | 0.0822192 | 40 | 2.0400000 | 0.0885438 | 40 | 3.816 |
| 26 | 10 | Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady | 2009 | Gay 2009 | Callosobruchus maculatus | Beetle | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 90 | YES | 80 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 1.385 | 0.061 | 1 | 0.4500000 | 0.1201666 | 40 | 0.6400000 | 0.1517893 | 40 | 3.816 |
| 26 | 10 | Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady | 2009 | Gay 2009 | Callosobruchus maculatus | Beetle | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 90 | YES | 80 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.661 | 0.052 | 1 | 0.1571000 | 0.0059000 | 40 | 0.1626000 | 0.0103000 | 40 | 3.816 |
| 27 | 2 | Grazer, V. M., M. Demont, L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2014 | Grazer 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 39 | YES | 228 | Reproductive Success | B | NO | Direct | Stressed | 0.211 | 0.018 | 1 | 149.9000000 | 174.9000000 | 114 | 181.6000000 | 119.5000000 | 114 | 3.368 |
| 27 | 2 | Grazer, V. M., M. Demont, L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2014 | Grazer 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 39 | YES | 240 | Reproductive Success | B | NO | Direct | Unstressed | 0.214 | 0.017 | 1 | 240.6000000 | 189.7000000 | 120 | 291.5000000 | 275.6000000 | 120 | 3.368 |
| 28 | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Hangartner 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 49 | YES | 66 | Immunity | M | NO | Ambiguous | Unstressed | -0.141 | 0.059 | 1 | 6.9700000 | 1.7400000 | 33 | 6.7000000 | 2.0300000 | 33 | 2.591 |
| 28 | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Hangartner 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 49 | YES | 66 | Immunity | F | NO | Ambiguous | Unstressed | 0.848 | 0.065 | 1 | 6.3300000 | 1.3400000 | 33 | 7.7900000 | 2.0000000 | 33 | 2.591 |
| 28 | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Hangartner 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 49 | YES | 288 | Immunity | M | NO | Ambiguous | Stressed | 0.175 | 0.014 | 1 | 80.8600000 | 41.5400000 | 144 | 87.9200000 | 39.1000000 | 144 | 2.591 |
| 28 | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Hangartner 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 49 | YES | 288 | Immunity | F | NO | Ambiguous | Stressed | 0.089 | 0.014 | 1 | 85.0000000 | 41.5400000 | 144 | 88.9400000 | 46.4300000 | 144 | 2.591 |
| 28 | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Hangartner 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 49 | YES | 288 | Immunity | M | NO | Ambiguous | Unstressed | -0.097 | 0.014 | 1 | 92.8100000 | 35.8400000 | 144 | 89.2100000 | 38.2800000 | 144 | 2.591 |
| 28 | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Hangartner 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 49 | YES | 288 | Immunity | F | NO | Ambiguous | Unstressed | 0.070 | 0.014 | 1 | 87.9900000 | 35.8400000 | 144 | 90.2900000 | 29.3200000 | 144 | 2.591 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | M | NO | Ambiguous | Unstressed | -0.107 | 0.054 | 1 | 6.0300000 | 2.5400000 | 36 | 5.7500000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | F | NO | Ambiguous | Unstressed | 0.121 | 0.054 | 1 | 6.8100000 | 2.6200000 | 36 | 7.1300000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | B | NO | Ambiguous | Unstressed | -0.281 | 0.055 | 1 | 6.8400000 | 3.6700000 | 36 | 5.8200000 | 3.5000000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | M | NO | Ambiguous | Unstressed | -0.043 | 0.054 | 1 | 6.1400000 | 2.5400000 | 36 | 5.7500000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | F | NO | Ambiguous | Unstressed | -0.203 | 0.055 | 1 | 7.3400000 | 2.5400000 | 36 | 7.1300000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | B | NO | Ambiguous | Unstressed | -0.074 | 0.054 | 1 | 7.1100000 | 3.5000000 | 36 | 5.8200000 | 3.5000000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | M | NO | Ambiguous | Unstressed | -0.150 | 0.055 | 1 | 6.1400000 | 2.5400000 | 36 | 6.0300000 | 2.5400000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | F | NO | Ambiguous | Unstressed | -0.081 | 0.054 | 1 | 7.3400000 | 2.5400000 | 36 | 6.8100000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 72 | Immunity | B | NO | Ambiguous | Unstressed | -0.361 | 0.394 | 1 | 7.1100000 | 3.5000000 | 36 | 6.8400000 | 3.6700000 | 36 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | M | NO | Ambiguous | Stressed | -0.073 | 0.014 | 1 | 1.5000000 | 2.6800000 | 144 | 1.7100000 | 3.0500000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | F | NO | Ambiguous | Stressed | 0.035 | 0.014 | 1 | 1.4600000 | 2.9600000 | 144 | 1.3600000 | 2.7800000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | M | NO | Ambiguous | Unstressed | -0.164 | 0.014 | 1 | 2.1100000 | 3.3300000 | 144 | 1.6200000 | 2.5900000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | F | NO | Ambiguous | Unstressed | 0.022 | 0.014 | 1 | 2.6900000 | 4.3500000 | 144 | 2.7900000 | 4.8100000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | M | NO | Ambiguous | Stressed | 0.013 | 0.014 | 1 | 1.6700000 | 2.9600000 | 144 | 1.7100000 | 3.0500000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | F | NO | Ambiguous | Stressed | -0.025 | 0.014 | 1 | 1.4300000 | 2.7800000 | 144 | 1.3600000 | 2.7800000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | M | NO | Ambiguous | Unstressed | -0.029 | 0.014 | 1 | 2.2100000 | 3.5200000 | 144 | 1.6200000 | 2.5900000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | F | NO | Ambiguous | Unstressed | 0.068 | 0.014 | 1 | 2.4100000 | 3.8900000 | 144 | 2.7900000 | 4.8100000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | M | NO | Ambiguous | Stressed | -0.060 | 0.014 | 1 | 1.6700000 | 2.9600000 | 144 | 1.5000000 | 2.6800000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | F | NO | Ambiguous | Stressed | 0.010 | 0.014 | 1 | 1.4300000 | 2.7800000 | 144 | 1.4600000 | 2.9600000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | Immunity | M | NO | Ambiguous | Unstressed | -0.190 | 0.014 | 1 | 2.2100000 | 3.5200000 | 144 | 2.1100000 | 3.3300000 | 144 | 3.264 |
| 29 | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Hangartner 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 144 | Immunity | F | NO | Ambiguous | Unstressed | 0.087 | 0.014 | 1 | 2.4100000 | 3.8900000 | 144 | 2.6900000 | 4.3500000 | 144 | 3.264 |
| 30 | 17 | Holland, B. | 2002 | Holland 2002 | Drosophila melanogaster | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 38 | YES | 89 | Reproductive Success | F | NO | Direct | Stressed | -0.116 | 0.015 | 1 | 11.5900000 | 10.1800000 | 133 | 10.6600000 | 4.9500000 | 133 | 3.516 |
| 30 | 17 | Holland, B. | 2002 | Holland 2002 | Drosophila melanogaster | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 51 | YES | 89 | Reproductive Success | F | NO | Direct | Stressed | 0.070 | 0.015 | 1 | 14.4300000 | 3.2800000 | 133 | 14.8100000 | 6.9300000 | 133 | 3.516 |
| 31 | 18 | Holland, B. and W. R. Rice | 1999 | Holland 1999 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 47 | YES | 76 | Reproductive Success | F | NO | Direct | Stressed | -0.305 | 0.018 | 1 | 11.2400000 | 10.6600000 | 114 | 8.9300000 | 3.6000000 | 114 | 10.260 |
| 32 | 19 | Hollis, B., J. L. Fierst and D. Houle | 2009 | Hollis 2009 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 27 | Mutant Frequency | M | NO | Indirect | Stressed | 0.807 | 0.053 | -1 | NA | NA | NA | NA | NA | NA | 5.429 |
| 32 | 19 | Hollis, B., J. L. Fierst and D. Houle | 2009 | Hollis 2009 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 27 | Mutant Frequency | M | NO | Indirect | Unstressed | 0.237 | 0.049 | -1 | 0.9410000 | 1.7760000 | 40 | 0.3990000 | 2.6590000 | 40 | 5.429 |
| 33 | 19 | Hollis, B. and D. Houle | 2011 | Hollis 2011 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 60 | YES | 120 | Reproductive Success | B | NO | Direct | Stressed | -0.304 | 0.011 | 1 | 126.5900000 | 28.9794410 | 180 | 117.6000000 | 29.1136051 | 180 | 3.276 |
| 33 | 19 | Hollis, B. and D. Houle | 2011 | Hollis 2011 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 60 | YES | 164 | Reproductive Success | F | NO | Direct | Stressed | 0.031 | 0.008 | 1 | NA | NA | NA | NA | NA | NA | 3.276 |
| 33 | 19 | Hollis, B. and D. Houle | 2011 | Hollis 2011 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 60 | YES | 164 | Offspring Viability | F | NO | Direct | Stressed | -0.064 | 0.008 | 1 | NA | NA | NA | NA | NA | NA | 3.276 |
| 34 | 19 | Hollis, B. and T. J. Kawecki | 2014 | Hollis 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 100 | YES | 38 | Mating Latency | M | YES | Indirect | Stressed | 0.038 | 0.062 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 34 | 19 | Hollis, B. and T. J. Kawecki | 2014 | Hollis 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 100 | YES | 90 | Mating Latency | M | YES | Indirect | Stressed | 0.194 | 0.043 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 34 | 19 | Hollis, B. and T. J. Kawecki | 2014 | Hollis 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 100 | YES | 17 | Reproductive Success | M | NO | Direct | Stressed | 1.216 | 0.091 | 1 | 0.6010000 | 0.2950000 | 23 | 0.8760000 | 0.1380000 | 28 | 5.051 |
| 34 | 19 | Hollis, B. and T. J. Kawecki | 2014 | Hollis 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 100 | YES | 21 | Reproductive Success | M | NO | Direct | Stressed | 0.659 | 0.066 | -1 | 0.5530000 | 0.3660000 | 30 | 0.7710000 | 0.2860000 | 33 | 5.051 |
| 34 | 19 | Hollis, B. and T. J. Kawecki | 2014 | Hollis 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 100 | YES | 15 | Reproductive Success | M | NO | Direct | Stressed | 0.830 | 0.090 | -1 | 0.6100000 | 0.3400000 | 22 | 0.8530000 | 0.2300000 | 23 | 5.051 |
| 35 | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Hollis 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 139 | YES | 48 | Development Rate | M | NO | Ambiguous | Stressed | -0.482 | 0.028 | 1 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Hollis 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 139 | YES | 48 | Development Rate | F | NO | Ambiguous | Stressed | 0.414 | 0.028 | 1 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Hollis 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 162 | YES | 60 | Body Size | M | YES | Ambiguous | Stressed | 0.000 | 0.022 | 1 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Hollis 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 162 | YES | 60 | Body Size | F | YES | Ambiguous | Stressed | -0.238 | 0.022 | 1 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Hollis 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 117 | YES | 44 | Fitness Senescence | M | YES | Indirect | Stressed | 0.500 | 0.031 | -1 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Hollis 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 117 | YES | 45 | Fitness Senescence | F | YES | Indirect | Stressed | 0.017 | 0.030 | -1 | NA | NA | NA | NA | NA | NA | 4.201 |
| 36 | 29 | Immonen, E., R. R. Snook and M. G. Ritchie | 2014 | Immonen 2014 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 100 | YES | 30 | Reproductive Success | F | NO | Direct | Unstressed | 0.636 | 0.046 | 1 | NA | NA | NA | NA | NA | NA | 2.320 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Innocenti 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 30 | NO | 110 | Body Size | M | YES | Ambiguous | Unstressed | -0.306 | 0.120 | 1 | 780.1295325 | 24.5249313 | 169 | 773.1405125 | 20.8806168 | 160 | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Innocenti 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 30 | NO | 107 | Body Size | F | YES | Ambiguous | Unstressed | -0.290 | 0.013 | 1 | 879.0553188 | 25.2349182 | 160 | 870.6142500 | 32.5085570 | 160 | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Innocenti 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 30 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | 0.745 | 0.053 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Innocenti 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 31 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | 0.490 | 0.051 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Innocenti 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 50 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | 0.545 | 0.051 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Innocenti 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 58 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | 0.379 | 0.050 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Innocenti 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 30 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | -0.228 | 0.049 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Innocenti 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 31 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | 0.300 | 0.050 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Innocenti 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 50 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | -0.108 | 0.049 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Innocenti 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 58 | NO | 27 | Reproductive Success | F | NO | Direct | Unstressed | 0.080 | 0.049 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 38 | 3 | Jacomb, F., J. Marsh and L. Holman | 2016 | Jacomb 2016 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Blind | 5 | YES | 320 | Pesticide Resistance | B | NO | Ambiguous | Stressed | 1.246 | 0.005 | 1 | 0.8560000 | 0.0210000 | 480 | 0.8920000 | 0.0350000 | 480 | 4.201 |
| 38 | 3 | Jacomb, F., J. Marsh and L. Holman | 2016 | Jacomb 2016 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Blind | 5 | YES | 176 | Pesticide Resistance | B | NO | Ambiguous | Unstressed | 1.001 | 0.005 | 1 | 0.0880000 | 0.0850000 | 480 | 0.0270000 | 0.0140000 | 48 | 4.201 |
| 39 | 32 | Jarzebowska, M. and J. Radwan | 2010 | Jarzebowska 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 72 | Reproductive Success | F | NO | Direct | Stressed | 0.390 | 0.019 | -1 | 0.7390000 | 0.5240000 | 96 | 0.8080000 | 0.4010000 | 120 | 5.659 |
| 39 | 32 | Jarzebowska, M. and J. Radwan | 2010 | Jarzebowska 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 72 | Reproductive Success | F | NO | Direct | Unstressed | -0.190 | 0.020 | 1 | 0.8350000 | 0.4730000 | 96 | 0.7880000 | 0.5730000 | 120 | 5.659 |
| 39 | 32 | Jarzebowska, M. and J. Radwan | 2010 | Jarzebowska 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 11 | Extinction Rate | B | NO | Direct | Stressed | 0.752 | 0.133 | 1 | NA | NA | NA | NA | NA | NA | 5.659 |
| 39 | 32 | Jarzebowska, M. and J. Radwan | 2010 | Jarzebowska 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 72 | Offspring Viability | B | NO | Direct | Stressed | 0.150 | 0.019 | 1 | 37.9100000 | 27.9900000 | 96 | 51.3200000 | 38.5200000 | 120 | 5.659 |
| 39 | 32 | Jarzebowska, M. and J. Radwan | 2010 | Jarzebowska 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 72 | Offspring Viability | B | NO | Direct | Unstressed | -0.088 | 0.019 | 1 | 70.4400000 | 32.3000000 | 96 | 61.8700000 | 54.1700000 | 120 | 5.659 |
| 40 | 6 | Klemme, I. and R. C. Firman | 2013 | Klemme 2013 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 18 | YES | 12 | Reproductive Success | M | NO | Direct | Stressed | 0.769 | 0.114 | 1 | 0.2800000 | 0.4200000 | 18 | 0.7200000 | 0.6700000 | 18 | 3.068 |
| 40 | 6 | Klemme, I. and R. C. Firman | 2013 | Klemme 2013 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 18 | YES | 12 | Reproductive Success | M | NO | Direct | Unstressed | 0.946 | 0.119 | 1 | 0.3400000 | 0.3900000 | 18 | 0.7900000 | 0.5300000 | 18 | 3.068 |
| 41 | 4 | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Lumley 2015 | Tribolium castaneum | Beetle | 1.000 | 9.00 | 100.00 | 1 | 1 | Not Blind | 20 | NO | 56 | Reproductive Success | B | NO | Direct | Stressed | 0.576 | 0.025 | -1 | NA | NA | NA | NA | NA | NA | 38.138 |
| 41 | 4 | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Lumley 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 20 | YES | 16 | Reproductive Success | B | NO | Direct | Stressed | 0.559 | 0.084 | -1 | NA | NA | NA | NA | NA | NA | 38.138 |
| 41 | 4 | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Lumley 2015 | Tribolium castaneum | Beetle | 1.000 | 9.00 | 100.00 | 1 | 1 | Not Blind | 20 | NO | 56 | Extinction Rate | B | NO | Direct | Stressed | 0.522 | 0.024 | 1 | NA | NA | NA | NA | NA | NA | 38.138 |
| 41 | 4 | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Lumley 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 20 | YES | 16 | Extinction Rate | B | NO | Direct | Stressed | 0.798 | 0.087 | 1 | NA | NA | NA | NA | NA | NA | 38.138 |
| 42 | 11 | Maklakov, A. A., R. Bonduriansky and R. C. Brooks | 2009 | Maklakov 2009 | Callosobruchus maculatus | Beetle | 50.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 11 | YES | 11 | Reproductive Success | F | NO | Direct | Not Stated | -0.958 | 0.133 | 1 | 155.1200000 | 37.7600000 | 16 | 118.0000000 | 37.7600000 | 16 | 5.429 |
| 43 | 5 | Martin, O. Y. and D. J. Hosken | 2003 | Martin 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | 1 | 1 | Not Blind | 29 | YES | 10 | Lifespan | F | YES | Indirect | Unstressed | 0.841 | 0.138 | 1 | NA | NA | NA | NA | NA | NA | 3.833 |
| 43 | 5 | Martin, O. Y. and D. J. Hosken | 2003 | Martin 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | 1 | 1 | Not Blind | 29 | YES | 10 | Mating Success | M | NO | Indirect | Unstressed | 0.920 | 0.140 | 1 | NA | NA | NA | NA | NA | NA | 3.833 |
| 43 | 5 | Martin, O. Y. and D. J. Hosken | 2003 | Martin 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | 1 | 1 | Not Blind | 29 | YES | 10 | Reproductive Success | F | NO | Direct | Unstressed | 1.038 | 0.144 | 1 | 28.2000000 | 15.4532035 | 15 | 49.2000000 | 23.1604404 | 15 | 3.833 |
| 43 | 5 | Martin, O. Y. and D. J. Hosken | 2003 | Martin 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | 1 | 1 | Not Blind | 29 | YES | 10 | Lifespan | F | NO | Indirect | Stressed | -1.314 | 0.155 | 1 | 2.2130508 | 0.0600641 | 15 | 2.1161864 | 0.0817265 | 15 | 3.833 |
| 44 | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Martin 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | 1 | 1 | Not Blind | 42 | YES | 12 | Reproductive Success | F | NO | Direct | Unstressed | 0.421 | 0.159 | 1 | 34.9043478 | 21.5075526 | 12 | 42.9391304 | 14.7600851 | 12 | 3.833 |
| 44 | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Martin 2003 | Sepsis cynipsea | Fly | 250.000 | 1.00 | 500.00 | 1 | 1 | Not Blind | 42 | YES | 12 | Reproductive Success | F | NO | Direct | Unstressed | -0.075 | 0.155 | 1 | 34.9043478 | 21.5075526 | 12 | 33.4434783 | 15.6035186 | 12 | 3.833 |
| 44 | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Martin 2003 | Sepsis cynipsea | Fly | 10.000 | 1.00 | 500.00 | 1 | 1 | Not Blind | 42 | NO | 12 | Reproductive Success | F | NO | Direct | Unstressed | -0.603 | 0.163 | 1 | 42.9391304 | 14.7600851 | 12 | 33.4434783 | 15.6035186 | 12 | 3.833 |
| 44 | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Martin 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | 1 | 1 | Not Blind | 42 | YES | 24 | Lifespan | F | NO | Indirect | Unstressed | -0.405 | 0.082 | 1 | 17.3460898 | 2.4943223 | 24 | 16.3327787 | 2.4698682 | 24 | 3.833 |
| 44 | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Martin 2003 | Sepsis cynipsea | Fly | 250.000 | 1.00 | 500.00 | 1 | 1 | Not Blind | 42 | YES | 24 | Lifespan | F | NO | Indirect | Unstressed | -0.638 | 0.085 | 1 | 17.3460898 | 2.4943223 | 24 | 15.8086522 | 2.2497809 | 24 | 3.833 |
| 44 | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Martin 2003 | Sepsis cynipsea | Fly | 10.000 | 1.00 | 500.00 | 1 | 1 | Not Blind | 42 | NO | 24 | Lifespan | F | NO | Indirect | Unstressed | -0.216 | 0.081 | 1 | 16.3327787 | 2.4698682 | 24 | 15.8086522 | 2.2497809 | 24 | 3.833 |
| 45 | 33 | McGuigan, K., D. Petfield and M. W. Blows | 2011 | McGuigan 2011 | Drosophila serrata | Fly | 2.405 | 3.81 | 4.81 | 1 | 0 | Not Blind | 23 | YES | 292 | Mating Success | M | NO | Indirect | Stressed | 0.034 | 0.014 | 1 | 0.4997000 | 0.3400000 | 146 | 0.5097000 | 0.2460000 | 146 | 5.146 |
| 45 | 33 | McGuigan, K., D. Petfield and M. W. Blows | 2011 | McGuigan 2011 | Drosophila serrata | Fly | 2.405 | 3.81 | 4.81 | 1 | 0 | Not Blind | 26 | YES | 208 | Reproductive Success | F | NO | Direct | Stressed | 0.114 | 0.019 | 1 | 49.9300000 | 22.7000000 | 104 | 52.1740000 | 16.1000000 | 104 | 5.146 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | McKean 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 40 | Body Size | B | YES | Ambiguous | Unstressed | 1.528 | 0.242 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | McKean 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 40 | Development Rate | B | NO | Ambiguous | Unstressed | 0.853 | 0.105 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | McKean 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 40 | Development Rate | B | NO | Ambiguous | Unstressed | 3.124 | 0.218 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | McKean 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 40 | Development Rate | B | NO | Ambiguous | Unstressed | 2.655 | 0.184 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | McKean 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 52 | Mating Success | M | NO | Indirect | Unstressed | 0.839 | 0.081 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | McKean 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 52 | Mating Success | M | NO | Indirect | Unstressed | 1.598 | 0.099 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | McKean 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 52 | Mating Success | M | NO | Indirect | Unstressed | 1.907 | 0.110 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean, K. A. and L. Nunney | 2008 | McKean 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 80 | Immunity | B | NO | Ambiguous | Unstressed | -0.911 | 0.054 | -1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | McNamara 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 153 | Ejaculate Quality and Production | M | YES | Indirect | Unstressed | -0.106 | 0.027 | 1 | 2.7000000 | 0.8442748 | 88 | 2.6000000 | 1.0480935 | 65 | 4.259 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | McNamara 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 145 | Ejaculate Quality and Production | M | YES | Indirect | Unstressed | -0.157 | 0.028 | 1 | 0.1600000 | 0.0728835 | 83 | 0.1500000 | 0.0472440 | 62 | 4.259 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | McNamara 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 202 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.100 | 0.020 | 1 | 0.5700000 | 0.1014889 | 103 | 0.5800000 | 0.0994987 | 99 | 4.259 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | McNamara 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 101 | Ejaculate Quality and Production | M | YES | Indirect | Unstressed | -0.280 | 0.039 | 1 | 0.8600000 | 0.1428286 | 51 | 0.8200000 | 0.1414214 | 50 | 4.259 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | McNamara 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 127 | Mating Duration | M | YES | Ambiguous | Unstressed | -0.371 | 0.032 | 1 | 534.6200000 | 204.1600000 | 64 | 466.0200000 | 160.0944118 | 63 | 4.259 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | McNamara 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 127 | Reproductive Success | F | NO | Direct | Unstressed | 0.156 | 0.031 | 1 | 34.2600000 | 18.7200000 | 64 | 37.1700000 | 18.2556840 | 63 | 4.259 |
| 47 | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | McNamara 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 125 | Reproductive Success | M | NO | Direct | Unstressed | 0.315 | 0.032 | 1 | 0.6700000 | 0.3149603 | 62 | 0.7700000 | 0.3174902 | 63 | 4.259 |
| 48 | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | McNamara 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 1 | YES | 351 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.568 | 0.012 | 1 | 0.9400000 | 0.3200000 | 179 | 1.0800000 | 0.1300000 | 172 | 3.177 |
| 48 | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | McNamara 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 1 | YES | 336 | Immunity | M | NO | Ambiguous | Unstressed | 0.000 | 0.012 | 1 | 1.6500000 | 3.4000000 | 175 | 1.6500000 | 3.2000000 | 161 | 3.177 |
| 48 | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | McNamara 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 1 | YES | 413 | Immunity | F | NO | Ambiguous | Unstressed | -0.050 | 0.010 | 1 | 80.2000000 | 21.8000000 | 203 | 79.0500000 | 20.3000000 | 210 | 3.177 |
| 48 | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | McNamara 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 1 | YES | 788 | Immunity | B | NO | Ambiguous | Unstressed | -0.106 | 0.005 | -1 | NA | NA | NA | NA | NA | 401 | 3.177 |
| 48 | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | McNamara 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 1 | YES | 335 | Immunity | M | NO | Ambiguous | Unstressed | -0.108 | 0.012 | 1 | 0.5300000 | 0.1650000 | 173 | 0.5100000 | 0.2050000 | 162 | 3.177 |
| 48 | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | McNamara 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 1 | YES | 406 | Immunity | F | NO | Ambiguous | Unstressed | -0.098 | 0.010 | 1 | 0.5500000 | 0.2040000 | 202 | 0.5300000 | 0.2050000 | 204 | 3.177 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 47 | Mating Latency | M | YES | Indirect | Unstressed | 0.556 | 0.086 | 1 | 358.9000000 | 494.4000000 | 24 | 143.4000000 | 203.6000000 | 23 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 57 | Mating Latency | M | YES | Indirect | Unstressed | 0.470 | 0.070 | 1 | 294.7000000 | 313.6000000 | 28 | 158.0000000 | 259.1000000 | 29 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 53 | Mating Duration | M | YES | Ambiguous | Unstressed | 1.987 | 0.112 | 1 | 73.5000000 | 67.7000000 | 30 | 483.4000000 | 299.5000000 | 23 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 58 | Mating Duration | M | YES | Ambiguous | Unstressed | 0.551 | 0.070 | 1 | 181.8000000 | 198.5000000 | 29 | 323.3000000 | 298.6000000 | 29 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 53 | Mating Frequency | M | YES | Indirect | Unstressed | 1.982 | 0.112 | 1 | 2.1000000 | 2.2000000 | 30 | 22.2000000 | 15.0000000 | 23 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 58 | Mating Frequency | M | YES | Indirect | Unstressed | 0.929 | 0.075 | 1 | 4.2000000 | 4.1000000 | 29 | 15.0000000 | 15.7000000 | 29 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 30 | Reproductive Success | F | NO | Direct | Stressed | 1.852 | 0.183 | 1 | 183.8000000 | 80.6000000 | 15 | 409.5000000 | 147.0000000 | 15 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 30 | Reproductive Success | F | NO | Direct | Unstressed | 0.061 | 0.126 | 1 | 346.1000000 | 255.8000000 | 15 | 366.3000000 | 378.7000000 | 15 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.614 | 0.193 | 1 | 0.4570000 | 0.3580000 | 10 | 0.6320000 | 0.1450000 | 10 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.931 | 0.205 | 1 | 0.5290000 | 0.2500000 | 10 | 0.7200000 | 0.1210000 | 10 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 20 | Reproductive Success | M | NO | Direct | Unstressed | 0.319 | 0.186 | 1 | 0.5700000 | 0.0640000 | 10 | 0.6210000 | 0.2070000 | 10 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 24 | Reproductive Success | M | NO | Direct | Unstressed | -0.219 | 0.156 | 1 | 0.4530000 | 0.3920000 | 12 | 0.3610000 | 0.4180000 | 12 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 24 | Reproductive Success | M | NO | Direct | Unstressed | 0.178 | 0.156 | 1 | 0.4450000 | 0.4240000 | 12 | 0.5140000 | 0.3180000 | 12 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 24 | Reproductive Success | M | NO | Direct | Unstressed | 0.025 | 0.155 | -1 | 0.4210000 | 0.3560000 | 12 | 0.4300000 | 0.3340000 | 12 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 24 | Reproductive Success | M | NO | Direct | Unstressed | 0.110 | 0.156 | -1 | 0.7970000 | 0.3520000 | 12 | 0.8330000 | 0.2730000 | 12 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 24 | Reproductive Success | M | NO | Direct | Unstressed | 0.724 | 0.166 | 1 | 0.6940000 | 0.3350000 | 12 | 0.9010000 | 0.2000000 | 12 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 24 | Reproductive Success | M | NO | Direct | Unstressed | -0.389 | 0.159 | 1 | 0.8390000 | 0.2080000 | 12 | 0.7280000 | 0.3300000 | 12 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 30 | Lifespan | F | NO | Indirect | Stressed | 0.211 | 0.127 | 1 | 8.3000000 | 2.5500000 | 15 | 8.9000000 | 2.9700000 | 15 | 5.146 |
| 49 | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Michalczyk 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 29 | Lifespan | F | NO | Indirect | Unstressed | 0.677 | 0.138 | 1 | 8.8000000 | 2.7200000 | 14 | 10.3000000 | 1.4400000 | 15 | 5.146 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 27 | Mating Success | M | NO | Indirect | Unstressed | 0.657 | 0.076 | 1 | 0.2000000 | 0.1120000 | 27 | 0.2540000 | 0.1140000 | 27 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 29 | Mating Success | M | NO | Indirect | Unstressed | 0.041 | 0.069 | 1 | 0.8890000 | 0.0740000 | 28 | 0.8920000 | 0.0700000 | 30 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 31 | Mating Success | M | NO | Indirect | Unstressed | 0.211 | 0.063 | 1 | 0.8760000 | 0.1030000 | 31 | 0.9010000 | 0.1290000 | 31 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 145 | Mating Latency | M | YES | Indirect | Unstressed | -0.062 | 0.014 | 1 | 2.9400000 | 1.8800000 | 149 | 3.1200000 | 3.6900000 | 143 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 145 | Mating Duration | M | YES | Ambiguous | Unstressed | -0.918 | 0.015 | -1 | 11.7400000 | 2.3700000 | 149 | 14.0500000 | 2.6500000 | 143 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 122 | Mating Success | M | NO | Indirect | Unstressed | 0.153 | 0.016 | -1 | 0.0771000 | 0.1630000 | 122 | 0.1023000 | 0.1650000 | 121 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 27 | Mating Success | M | NO | Indirect | Unstressed | 0.314 | 0.073 | 1 | 0.1700000 | 0.0720000 | 27 | 0.2000000 | 0.1120000 | 27 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 29 | Mating Success | M | NO | Indirect | Unstressed | 0.613 | 0.073 | 1 | 0.8350000 | 0.0980000 | 28 | 0.8890000 | 0.0740000 | 28 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 31 | Mating Success | M | NO | Indirect | Unstressed | -0.178 | 0.064 | 1 | 0.8970000 | 0.1290000 | 30 | 0.8760000 | 0.1030000 | 31 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 145 | Mating Latency | M | YES | Indirect | Unstressed | 0.159 | 0.014 | 1 | 3.5600000 | 5.2200000 | 142 | 2.9400000 | 1.8800000 | 149 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 145 | Mating Duration | M | YES | Ambiguous | Unstressed | 0.471 | 0.014 | 1 | 12.8800000 | 2.4600000 | 142 | 11.7400000 | 2.3700000 | 149 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 122 | Mating Success | M | NO | Indirect | Unstressed | 0.170 | 0.016 | -1 | 0.0543000 | 0.0961000 | 122 | 0.0771000 | 0.1630000 | 122 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 27 | Mating Success | M | NO | Indirect | Unstressed | 0.868 | 0.079 | 1 | 0.1700000 | 0.0720000 | 27 | 0.2540000 | 0.1140000 | 27 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 29 | Mating Success | M | NO | Indirect | Unstressed | 0.660 | 0.073 | 1 | 0.8350000 | 0.0980000 | 28 | 0.8920000 | 0.0700000 | 30 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 31 | Mating Success | M | NO | Indirect | Unstressed | 0.031 | 0.064 | 1 | 0.8970000 | 0.1290000 | 30 | 0.9010000 | 0.1290000 | 31 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 145 | Mating Latency | M | YES | Indirect | Unstressed | 0.097 | 0.014 | 1 | 3.5600000 | 5.2200000 | 142 | 3.1200000 | 3.6900000 | 143 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 145 | Mating Duration | M | YES | Ambiguous | Unstressed | -0.456 | 0.014 | -1 | 12.8800000 | 2.4600000 | 142 | 14.0500000 | 2.6500000 | 143 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 122 | Mating Success | M | NO | Indirect | Unstressed | 0.355 | 0.017 | -1 | 0.0543000 | 0.0961000 | 122 | 0.1023000 | 0.1650000 | 121 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 1440 | Offspring Viability | B | NO | Direct | Unstressed | 0.088 | 0.001 | 1 | 0.8700000 | 0.3415260 | 1440 | 0.9000000 | 0.3415260 | 1440 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 1440 | Offspring Viability | B | NO | Direct | Unstressed | -0.088 | 0.001 | 1 | 0.9000000 | 0.3415260 | 1440 | 0.8700000 | 0.3415260 | 1440 | 4.659 |
| 50 | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Nandy 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 1440 | Offspring Viability | B | NO | Direct | Unstressed | 0.000 | 0.001 | 1 | 0.9000000 | 0.3415260 | 1440 | 0.9000000 | 0.3415260 | 1440 | 4.659 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 27 | Body Size | F | YES | Ambiguous | Unstressed | -0.089 | 0.072 | 1 | 0.2826667 | 0.0124900 | 27 | 0.2822222 | 0.0101274 | 27 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 27 | Body Size | F | YES | Ambiguous | Unstressed | -0.981 | 0.081 | 1 | 0.2943519 | 0.0103532 | 27 | 0.2826667 | 0.0124900 | 27 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 27 | Body Size | F | YES | Ambiguous | Unstressed | -1.183 | 0.085 | -1 | 0.2943519 | 0.0103532 | 27 | 0.2822222 | 0.0101274 | 27 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 30 | Mating Frequency | F | YES | Indirect | Unstressed | -0.090 | 0.065 | 1 | 6.7439524 | 3.1492291 | 30 | 7.0200794 | 2.9816484 | 30 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 30 | Mating Frequency | F | YES | Indirect | Unstressed | 0.284 | 0.066 | 1 | 7.6940238 | 3.4353720 | 30 | 6.7439524 | 3.1492291 | 30 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 30 | Mating Frequency | F | YES | Indirect | Unstressed | 0.205 | 0.065 | 1 | 7.6940238 | 3.4353720 | 30 | 7.0200794 | 2.9816484 | 30 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 29 | Reproductive Success | F | NO | Direct | Unstressed | 0.185 | 0.067 | 1 | 50.3663793 | 7.6774347 | 29 | 51.5985906 | 5.1763110 | 29 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 29 | Reproductive Success | F | NO | Direct | Unstressed | -0.890 | 0.075 | -1 | 56.1414116 | 4.7002918 | 28 | 50.3663793 | 7.6774347 | 29 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 29 | Reproductive Success | F | NO | Direct | Unstressed | -0.905 | 0.075 | -1 | 56.1414116 | 4.7002918 | 28 | 51.5985906 | 5.1763110 | 29 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 28 | Reproductive Success | F | NO | Direct | Stressed | 0.771 | 0.076 | 1 | 47.9508929 | 7.0792749 | 28 | 52.6177249 | 4.5419731 | 27 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 28 | Reproductive Success | F | NO | Direct | Stressed | 0.168 | 0.067 | 1 | 42.6208333 | 8.3991535 | 30 | 47.9508929 | 7.0792749 | 28 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 28 | Reproductive Success | F | NO | Direct | Stressed | 1.439 | 0.087 | 1 | 42.6208333 | 8.3991535 | 30 | 52.6177249 | 4.5419731 | 27 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 29 | Lifespan | F | NO | Indirect | Unstressed | 1.315 | 0.081 | 1 | 33.4558333 | 4.1586802 | 30 | 38.8756979 | 3.9717452 | 29 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 27 | Lifespan | F | NO | Indirect | Unstressed | 0.189 | 0.074 | 1 | 33.2781463 | 4.5549330 | 28 | 33.4558333 | 4.1586802 | 30 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 29 | Lifespan | F | NO | Indirect | Unstressed | 0.041 | 0.067 | -1 | 33.2781463 | 4.5549330 | 28 | 38.8756979 | 3.9717452 | 29 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 27 | Lifespan | F | NO | Indirect | Unstressed | -0.669 | 0.078 | 1 | 56.2509143 | 5.8375189 | 25 | 57.2156463 | 4.2069037 | 28 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 29 | Lifespan | F | NO | Indirect | Unstressed | 1.295 | 0.083 | 1 | 60.1348639 | 5.1204446 | 28 | 56.2509143 | 5.8375189 | 25 | 4.612 |
| 51 | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Nandy 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 27 | Lifespan | F | NO | Indirect | Unstressed | -0.612 | 0.073 | 1 | 60.1348639 | 5.1204446 | 28 | 57.2156463 | 4.2069037 | 28 | 4.612 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Nelson 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 3 | YES | 20 | Body Size | M | YES | Ambiguous | Unstressed | -0.831 | 0.201 | 1 | NA | NA | NA | NA | NA | NA | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Nelson 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 3 | YES | 20 | Body Size | F | YES | Ambiguous | Unstressed | -0.831 | 0.201 | 1 | NA | NA | NA | NA | NA | NA | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Nelson 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 3 | YES | 20 | Male Attractiveness | M | NO | Indirect | Unstressed | 1.999 | 0.283 | 1 | 0.3850000 | 0.1090000 | 10 | 0.6210000 | 0.1170000 | 10 | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Nelson 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 2 | YES | 100 | Reproductive Success | M | NO | Direct | Unstressed | 0.415 | 0.040 | 1 | NA | NA | NA | NA | NA | NA | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Nelson 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 2 | YES | 200 | Reproductive Success | F | NO | Direct | Unstressed | -0.118 | 0.020 | 1 | NA | NA | NA | NA | NA | NA | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Nelson 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 2 | YES | 12 | Reproductive Success | M | NO | Direct | Stressed | 0.835 | 0.313 | 1 | 4.5300000 | 3.5000000 | 6 | 9.5400000 | 7.0100000 | 6 | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Nelson 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 2 | YES | 12 | Reproductive Success | M | NO | Direct | Unstressed | 0.849 | 0.314 | 1 | 13.5000000 | 11.5700000 | 6 | 23.1800000 | 9.3500000 | 6 | 3.407 |
| 52 | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Nelson 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 2 | YES | 100 | Offspring Viability | M | NO | Direct | Unstressed | -0.304 | 0.041 | 1 | NA | NA | NA | NA | NA | NA | 3.407 |
| 53 | 23 | Partridge, L. | 1980 | Partridge 1980 | Drosophila melanogaster | Fly | 100.000 | 1.00 | 200.00 | 1 | 1 | Not Blind | 1 | YES | 41 | Offspring Viability | B | NO | Direct | Unstressed | 0.773 | 0.103 | 1 | 48.9000000 | 2.9495762 | 18 | 51.1000000 | 2.6645825 | 23 | NA |
| 53 | 23 | Partridge, L. | 1980 | Partridge 1980 | Drosophila melanogaster | Fly | 100.000 | 1.00 | 200.00 | 1 | 1 | Not Blind | 1 | YES | 35 | Offspring Viability | B | NO | Direct | Unstressed | 0.874 | 0.125 | 1 | 48.1000000 | 2.4083189 | 14 | 49.8000000 | 1.4832397 | 21 | NA |
| 53 | 23 | Partridge, L. | 1980 | Partridge 1980 | Drosophila melanogaster | Fly | 100.000 | 1.00 | 200.00 | 1 | 1 | Not Blind | 1 | YES | 60 | Offspring Viability | B | NO | Direct | Unstressed | 0.707 | 0.069 | 1 | 49.4400000 | 1.4142136 | 32 | 50.4500000 | 1.4142136 | 28 | NA |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Pelabon 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 171 | Body Size | F | YES | Ambiguous | Unstressed | 0.080 | 0.023 | 1 | 25.0000000 | 4.3826932 | 80 | 25.3600000 | 4.5789082 | 91 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Pelabon 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 284 | Body Size | M | YES | Ambiguous | Unstressed | 0.019 | 0.014 | 1 | 16.1800000 | 1.6099182 | 127 | 16.2100000 | 1.5982097 | 157 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Pelabon 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 284 | Male Attractiveness | M | NO | Indirect | Unstressed | 0.120 | 0.014 | 1 | 1.5900000 | 0.8624562 | 127 | 1.7000000 | 0.9589258 | 157 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Pelabon 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 284 | Male Attractiveness | M | NO | Indirect | Unstressed | 0.000 | 0.014 | 1 | 3.1300000 | 0.1437427 | 127 | 3.1300000 | 0.1278568 | 157 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Pelabon 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 284 | Male Attractiveness | M | NO | Indirect | Unstressed | 0.193 | 0.014 | 1 | 0.1600000 | 0.8624562 | 127 | 0.3300000 | 0.8949974 | 157 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Pelabon 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 284 | Male Attractiveness | M | NO | Indirect | Unstressed | 0.055 | 0.014 | 1 | 150.8900000 | 7.9058485 | 127 | 151.3400000 | 8.2900267 | 157 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Pelabon 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 174 | Reproductive Success | F | NO | Direct | Unstressed | -0.277 | 0.023 | 1 | 1.5900000 | 0.7244860 | 80 | 1.3820000 | 0.7659334 | 94 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Pelabon 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 173 | Offspring Viability | F | YES | Direct | Unstressed | 0.621 | 0.024 | 1 | 6.9400000 | 0.5992662 | 80 | 7.3200000 | 0.6171936 | 93 | 3.232 |
| 54 | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Pelabon 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 145 | Offspring Viability | F | NO | Direct | Unstressed | 0.010 | 0.027 | 1 | 3.3200000 | 2.8195212 | 73 | 3.3500000 | 2.9698485 | 72 | 3.232 |
| 55 | 24 | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Pitnick 2001a | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 84 | YES | 228 | Body Size | F | YES | Ambiguous | Unstressed | 0.973 | 0.020 | 1 | 0.8790000 | 0.0427083 | 114 | 0.9210000 | 0.0427083 | 114 | NA |
| 55 | 24 | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Pitnick 2001a | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 84 | YES | 234 | Body Size | F | YES | Ambiguous | Unstressed | 0.763 | 0.018 | 1 | 0.8950000 | 0.0432666 | 117 | 0.9240000 | 0.0324500 | 117 | NA |
| 55 | 24 | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Pitnick 2001a | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 84 | YES | 230 | Reproductive Success | F | NO | Direct | Unstressed | -0.363 | 0.018 | 1 | 129.1000000 | 80.4285397 | 115 | 99.0000000 | 84.7180618 | 115 | NA |
| 55 | 24 | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Pitnick 2001a | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 84 | YES | 236 | Reproductive Success | F | NO | Direct | Unstressed | -0.246 | 0.017 | 1 | 122.0000000 | 86.9022439 | 118 | 101.2000000 | 81.4708537 | 118 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 61 | YES | 100 | Body Size | M | YES | Ambiguous | Unstressed | 2.115 | 0.062 | 1 | 233.1300000 | 16.9400000 | 50 | 270.8300000 | 18.4100000 | 50 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 61 | YES | 100 | Body Size | M | YES | Ambiguous | Unstressed | 1.346 | 0.048 | 1 | 211.6700000 | 19.8900000 | 50 | 237.1900000 | 17.6800000 | 50 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 61 | YES | 100 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 2.886 | 0.081 | 1 | 8.7307692 | 1.5410000 | 50 | 13.7564103 | 1.9037490 | 50 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 61 | YES | 100 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.596 | 0.041 | 1 | 7.7820513 | 2.2663679 | 50 | 9.0897436 | 2.0850585 | 50 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 61 | YES | 30 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 1.069 | 0.145 | 1 | 25.5723951 | 4.4651987 | 15 | 30.4600812 | 4.4023085 | 15 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 61 | YES | 30 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 1.484 | 0.163 | 1 | 27.4722598 | 3.3331765 | 15 | 32.9769959 | 3.8991876 | 15 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 81 | YES | 30 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.175 | 0.127 | 1 | 177.4228571 | 4.2492160 | 15 | 178.1600000 | 3.9836400 | 15 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 81 | YES | 30 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | -1.448 | 0.161 | 1 | 179.7885714 | 3.1869120 | 15 | 174.8857143 | 3.3860940 | 15 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 81 | YES | 178 | Mating Success | M | NO | Indirect | Unstressed | 0.015 | 0.022 | 1 | NA | NA | NA | NA | NA | NA | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 81 | YES | 180 | Mating Success | M | NO | Indirect | Unstressed | 0.148 | 0.022 | 1 | NA | NA | NA | NA | NA | NA | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 66 | YES | 140 | Reproductive Success | M | NO | Direct | Unstressed | -0.436 | 0.029 | 1 | NA | NA | NA | NA | NA | NA | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 38 | YES | 315 | Reproductive Success | M | NO | Direct | Unstressed | 0.022 | 0.014 | 1 | 0.5878581 | 0.3673826 | 112 | 0.5976808 | 0.4837226 | 203 | NA |
| 56 | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Pitnick 2001b | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 38 | YES | 344 | Reproductive Success | M | NO | Direct | Unstressed | 0.327 | 0.012 | 1 | 0.4503411 | 0.4170165 | 162 | 0.5968622 | 0.4754863 | 182 | NA |
| 57 | 32 | Plesnar, A., M. Konior and J. Radwan | 2011 | Plesnar 2011 | Rhizoglyphus robini | Mite | 1.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 2 | NO | 80 | Offspring Viability | M | NO | Direct | Stressed | 0.060 | 0.049 | 1 | 0.7700000 | 0.1700000 | 40 | 0.7800000 | 0.1600000 | 40 | 1.029 |
| 57 | 32 | Plesnar, A., M. Konior and J. Radwan | 2011 | Plesnar 2011 | Rhizoglyphus robini | Mite | 1.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 2 | NO | 80 | Offspring Viability | M | NO | Direct | Unstressed | -0.094 | 0.049 | 1 | 0.9500000 | 0.1100000 | 40 | 0.9400000 | 0.1000000 | 40 | 1.029 |
| 58 | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Plesnar-Bielak 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | 1 | 1 | Not Blind | 14 | YES | 60 | Reproductive Success | F | NO | Direct | Stressed | 1.504 | 0.127 | 1 | 31.0909091 | 15.1950949 | 11 | 92.8571429 | 43.4161068 | 49 | 5.683 |
| 58 | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Plesnar-Bielak 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | 1 | 1 | Not Blind | 14 | YES | 95 | Reproductive Success | F | NO | Direct | Stressed | 1.171 | 0.071 | 1 | 134.5000000 | 48.3000374 | 48 | 143.1428571 | 49.8409939 | 56 | 5.683 |
| 58 | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Plesnar-Bielak 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | 1 | 1 | Not Blind | 14 | YES | 104 | Reproductive Success | F | NO | Direct | Stressed | 0.174 | 0.038 | 1 | NA | NA | NA | NA | NA | NA | 5.683 |
| 58 | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Plesnar-Bielak 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | 1 | 1 | Not Blind | 14 | YES | 117 | Reproductive Success | F | NO | Direct | Stressed | 0.526 | 0.120 | 1 | NA | NA | NA | NA | NA | NA | 5.683 |
| 58 | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Plesnar-Bielak 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | 1 | 1 | Not Blind | 14 | YES | 11 | Extinction Rate | B | NO | Direct | Stressed | 1.510 | 0.740 | 1 | NA | NA | NA | NA | NA | NA | 5.683 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Power 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 32 | Reproductive Success | F | NO | Direct | Stressed | 1.331 | 0.148 | 1 | 741.0000000 | 154.4321210 | 18 | 948.0000000 | 147.7954668 | 14 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Power 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 32 | Reproductive Success | F | NO | Direct | Stressed | 1.339 | 0.149 | 1 | 37.0000000 | 7.6367532 | 18 | 47.4000000 | 7.4833148 | 14 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Power 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 36 | Reproductive Success | F | NO | Direct | Unstressed | 1.242 | 0.128 | 1 | 602.0000000 | 143.8255193 | 18 | 752.0000000 | 84.8528137 | 18 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Power 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 36 | Reproductive Success | F | NO | Direct | Unstressed | 1.240 | 0.128 | 1 | 30.1000000 | 7.2124892 | 18 | 37.6000000 | 4.2426407 | 18 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Power 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 32 | Offspring Viability | F | NO | Direct | Stressed | 1.465 | 0.154 | 1 | 765.0000000 | 156.9777054 | 18 | 978.0000000 | 118.9847049 | 14 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Power 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 32 | Offspring Viability | F | NO | Direct | Stressed | 1.428 | 0.153 | 1 | 38.3000000 | 8.0610173 | 18 | 48.9000000 | 5.9866518 | 14 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Power 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 32 | Offspring Viability | B | NO | Direct | Stressed | 1.017 | 0.137 | 1 | 70.4000000 | 9.7580736 | 18 | 79.1000000 | 5.9866518 | 14 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Power 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 36 | Offspring Viability | F | NO | Direct | Unstressed | 1.194 | 0.126 | 1 | 674.0000000 | 181.5850214 | 18 | 852.0000000 | 97.5807358 | 18 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Power 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 36 | Offspring Viability | F | NO | Direct | Unstressed | 1.223 | 0.127 | 1 | 33.7000000 | 8.9095454 | 18 | 42.6000000 | 4.6669048 | 18 | 2.747 |
| 59 | 13 | Power, D. J. and L. Holman | 2014 | Power 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 36 | Offspring Viability | B | NO | Direct | Unstressed | 1.050 | 0.122 | 1 | 73.0000000 | 7.6367532 | 18 | 79.8000000 | 4.6669048 | 18 | 2.747 |
| 60 | 13 | Power, D. J. and L. Holman | 2015 | Power 2014 | Callosobruchus maculatus | Beetle | 2.000 | 3.00 | 4.00 | 1 | 0 | Blind | 3 | YES | 39 | Reproductive Success | F | NO | Direct | Unstressed | 0.160 | 0.099 | 1 | 0.6091667 | 0.1700941 | 20 | 0.5425014 | 0.1557092 | 19 | 2.747 |
| 60 | 13 | Power, D. J. and L. Holman | 2015 | Power 2014 | Callosobruchus maculatus | Beetle | 2.000 | 3.00 | 4.00 | 1 | 0 | Blind | 3 | YES | 39 | Offspring Viability | F | NO | Direct | Unstressed | -0.396 | 0.100 | 1 | 39.4500000 | 15.0559483 | 20 | 41.7368421 | 12.7446813 | 19 | 2.747 |
| 61 | 25 | Promislow, D. E. L., E. A. Smith and L. Pearse | 1998 | Promislow 1998 | Drosophila melanogaster | Fly | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 13 | YES | 150 | Body Size | M | YES | Ambiguous | Unstressed | 0.100 | 0.026 | 1 | -0.0125000 | 0.2600000 | 75 | 0.0168000 | 0.3190000 | 75 | 9.821 |
| 61 | 25 | Promislow, D. E. L., E. A. Smith and L. Pearse | 1998 | Promislow 1998 | Drosophila melanogaster | Fly | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 13 | YES | 150 | Body Size | F | YES | Ambiguous | Unstressed | -0.449 | 0.027 | 1 | 0.0950000 | 0.1750000 | 75 | -0.0870000 | 0.5430000 | 75 | 9.821 |
| 61 | 25 | Promislow, D. E. L., E. A. Smith and L. Pearse | 1998 | Promislow 1998 | Drosophila melanogaster | Fly | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 17 | YES | 10182 | Offspring Viability | B | NO | Direct | Unstressed | 0.006 | 0.001 | 1 | NA | NA | NA | NA | NA | NA | 9.821 |
| 62 | 32 | Radwan, J. | 2004 | Radwan 2004a | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 2 | YES | 50 | Offspring Viability | B | NO | Direct | Stressed | 0.739 | 0.118 | 1 | 42.1100000 | 32.8700000 | 39 | 65.3900000 | 22.6900000 | 11 | 3.914 |
| 63 | 32 | Radwan, J., J. Unrug, K. Sigorska and K. Gawronska | 2004 | Radwan 2004b | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 11 | YES | 92 | Reproductive Success | F | NO | Direct | Unstressed | -0.142 | 0.043 | 1 | 112.7000000 | 25.1624442 | 46 | 108.7000000 | 30.3170150 | 46 | 2.893 |
| 63 | 32 | Radwan, J., J. Unrug, K. Sigorska and K. Gawronska | 2004 | Radwan 2004b | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 11 | YES | 66 | Reproductive Success | M | NO | Direct | Unstressed | -0.123 | 0.059 | 1 | 0.6170000 | 0.7180703 | 33 | 0.5430000 | 0.4423313 | 33 | 2.893 |
| 63 | 32 | Radwan, J., J. Unrug, K. Sigorska and K. Gawronska | 2004 | Radwan 2004b | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 11 | YES | 106 | Offspring Viability | B | NO | Direct | Unstressed | 0.106 | 0.037 | 1 | 0.7030000 | 0.1965630 | 53 | 0.7610000 | 0.7425712 | 53 | 2.893 |
| 63 | 32 | Radwan, J., J. Unrug, K. Sigorska and K. Gawronska | 2004 | Radwan 2004b | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 11 | YES | 90 | Lifespan | F | NO | Indirect | Unstressed | -0.085 | 0.044 | 1 | 25.3700000 | 21.1979244 | 45 | 23.7400000 | 16.4350996 | 45 | 2.893 |
| 64 | 34 | Rundle, H. D., S. F. Chenoweth and M. W. Blows | 2006 | Rundle 2006 | Drosophila serrata | Fly | 55.000 | 1.00 | 110.00 | 1 | 1 | Not Blind | 16 | YES | 552 | Reproductive Success | B | NO | Direct | Stressed | -0.067 | 0.007 | 1 | 30.4100000 | 40.5200000 | 276 | 27.6800000 | 40.5200000 | 276 | 4.292 |
| 64 | 34 | Rundle, H. D., S. F. Chenoweth and M. W. Blows | 2006 | Rundle 2006 | Drosophila serrata | Fly | 55.000 | 1.00 | 110.00 | 1 | 1 | Not Blind | 16 | YES | 552 | Reproductive Success | B | NO | Direct | Unstressed | -0.028 | 0.007 | 1 | 19.5700000 | 23.5600000 | 276 | 18.8300000 | 28.2700000 | 276 | 4.292 |
| 66 | 37 | Simmons, L. W. and F. Garcia-Gonzalez | 2008 | Simmons 2008 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 20 | YES | 88 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.918 | 0.049 | 1 | 2.1300000 | 0.5969925 | 44 | 2.6000000 | 0.3979950 | 44 | 4.737 |
| 66 | 37 | Simmons, L. W. and F. Garcia-Gonzalez | 2008 | Simmons 2008 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 20 | YES | 88 | Body Condition | M | NO | Indirect | Unstressed | -0.727 | 0.048 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Tilszer 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 120 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.205 | 0.033 | 1 | 86.7000000 | 40.2790268 | 60 | 95.5000000 | 44.9266068 | 60 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Tilszer 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 120 | Early Fecundity | F | YES | Ambiguous | Unstressed | 0.259 | 0.033 | 1 | 90.7000000 | 52.6725735 | 60 | 102.4000000 | 35.6314468 | 60 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Tilszer 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 120 | Mating Success | M | NO | Indirect | Unstressed | 1.768 | 0.046 | 1 | 0.4310000 | 0.1006976 | 60 | 0.6170000 | 0.1084435 | 60 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Tilszer 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 120 | Mating Success | M | NO | Indirect | Unstressed | 0.282 | 0.033 | 1 | 0.4760000 | 0.5654556 | 60 | 0.6340000 | 0.5499636 | 60 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Tilszer 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 120 | Reproductive Success | F | NO | Direct | Unstressed | 0.022 | 0.033 | 1 | 284.3000000 | 105.3451470 | 60 | 286.8000000 | 120.8370804 | 60 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Tilszer 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 120 | Reproductive Success | F | NO | Direct | Unstressed | 0.123 | 0.033 | 1 | 278.0000000 | 61.1931369 | 60 | 284.4000000 | 39.5044301 | 60 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Tilszer 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 42 | Offspring Viability | F | NO | Direct | Unstressed | -0.287 | 0.093 | 1 | 97.8000000 | 0.4582576 | 21 | 97.5000000 | 1.3747727 | 21 | 4.292 |
| 67 | 32 | Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan | 2006 | Tilszer 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 42 | Offspring Viability | F | NO | Direct | Unstressed | -0.199 | 0.092 | 1 | 97.4000000 | 0.9165151 | 21 | 96.6000000 | 5.4990908 | 21 | 4.292 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | van Lieshout 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 99 | Behavioural Plasticity | F | YES | Ambiguous | Unstressed | -0.018 | 0.040 | 1 | 285.4200000 | 196.1144075 | 50 | 282.2448980 | 155.8838417 | 49 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | van Lieshout 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 98 | Behavioural Plasticity | F | YES | Ambiguous | Unstressed | -0.132 | 0.040 | 1 | 393.4166667 | 153.0849901 | 48 | 371.1800000 | 179.6165633 | 50 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | van Lieshout 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 99 | Body Size | M | YES | Ambiguous | Unstressed | 0.155 | 0.040 | 1 | 3.4253300 | 0.5533225 | 50 | 3.5132898 | 0.4702925 | 49 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | van Lieshout 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 98 | Body Size | F | YES | Ambiguous | Unstressed | 0.259 | 0.041 | 1 | 4.4623021 | 0.6760828 | 48 | 4.6295060 | 0.6208700 | 50 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | van Lieshout 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 99 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | 0.116 | 0.040 | 1 | 0.2007420 | 0.0585906 | 50 | 0.2075663 | 0.0648678 | 49 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | van Lieshout 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 98 | Ejaculate Quality and Production | M | NO | Indirect | Unstressed | -0.022 | 0.040 | 1 | 0.1668542 | 0.0523804 | 48 | 0.1663250 | 0.0433648 | 50 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | van Lieshout 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 99 | Mating Latency | M | YES | Indirect | Unstressed | 0.084 | 0.040 | -1 | 49.2000000 | 73.2039365 | 50 | 44.1836735 | 40.4874844 | 49 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | van Lieshout 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 98 | Mating Latency | F | YES | Indirect | Unstressed | -0.105 | 0.040 | 1 | 69.6458333 | 86.1992964 | 48 | 61.4200000 | 68.2764758 | 50 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | van Lieshout 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 96 | Immunity | M | NO | Ambiguous | Unstressed | -0.373 | 0.042 | 1 | 12.7920000 | 0.3350000 | 49 | 12.6780000 | 0.2580000 | 47 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | van Lieshout 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 94 | Immunity | F | NO | Ambiguous | Unstressed | -0.564 | 0.044 | 1 | 12.9760000 | 0.2400000 | 47 | 12.8530000 | 0.1880000 | 47 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | van Lieshout 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 99 | Mating Duration | M | YES | Ambiguous | Unstressed | 0.029 | 0.040 | 1 | 565.1000000 | 277.6167708 | 50 | 572.7551020 | 244.4586307 | 49 | 4.612 |
| 68 | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | van Lieshout 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 98 | Mating Duration | F | YES | Ambiguous | Unstressed | 0.354 | 0.041 | -1 | 616.3958333 | 261.4579206 | 48 | 530.8400000 | 217.4206268 | 50 | 4.612 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 31 | NO | 180 | Mating Frequency | F | YES | Indirect | Unstressed | -0.236 | 0.011 | -1 | 0.3000000 | 0.5366563 | 180 | 0.6700000 | 2.1466253 | 180 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 900 | Mating Frequency | M | YES | Indirect | Unstressed | 0.161 | 0.002 | 1 | 0.0390000 | 0.0900000 | 900 | 0.0650000 | 0.2100000 | 900 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 31 | NO | 180 | Mating Frequency | F | YES | Indirect | Unstressed | -0.178 | 0.011 | 1 | 0.2300000 | 0.1341641 | 180 | 0.3000000 | 0.5366563 | 180 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 900 | Mating Frequency | M | YES | Indirect | Unstressed | -0.077 | 0.002 | 1 | 0.0530000 | 0.2400000 | 900 | 0.2300000 | 0.1341641 | 180 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 31 | NO | 180 | Mating Frequency | F | YES | Indirect | Unstressed | -0.288 | 0.011 | 1 | 0.2300000 | 0.1341641 | 180 | 0.6700000 | 2.1466253 | 180 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 900 | Mating Frequency | M | YES | Indirect | Unstressed | 0.053 | 0.002 | 1 | 0.0530000 | 0.2400000 | 900 | 0.0650000 | 0.2100000 | 900 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | 0.009 | 0.126 | 1 | 91.0000000 | 68.9050989 | 15 | 91.5000000 | 35.8880723 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.235 | 0.127 | 1 | 83.0000000 | 54.5498700 | 15 | 68.0000000 | 68.9050989 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.259 | 0.127 | 1 | 98.0000000 | 54.5498700 | 15 | 81.0000000 | 71.7761447 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.094 | 0.126 | 1 | 90.5000000 | 49.5255398 | 15 | 86.0000000 | 43.0656868 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | 0.119 | 0.126 | 1 | 76.0000000 | 85.4136122 | 15 | 84.5000000 | 48.8077784 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.397 | 0.129 | 1 | 96.5000000 | 68.9050989 | 15 | 73.5000000 | 40.1946410 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | 0.057 | 0.126 | 1 | 88.0000000 | 22.9683663 | 15 | 91.0000000 | 68.9050989 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.201 | 0.127 | 1 | 92.0000000 | 28.7104579 | 15 | 83.0000000 | 54.5498700 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | 0.132 | 0.127 | 1 | 88.0000000 | 89.0024194 | 15 | 98.0000000 | 54.5498700 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.006 | 0.126 | 1 | 91.0000000 | 114.8418315 | 15 | 90.5000000 | 49.5255398 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.171 | 0.127 | 1 | 89.0000000 | 60.2919615 | 15 | 76.0000000 | 85.4136122 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | 0.118 | 0.126 | 1 | 88.5000000 | 63.1630073 | 15 | 96.5000000 | 68.9050989 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | 0.113 | 0.126 | 1 | 88.0000000 | 22.9683663 | 15 | 91.5000000 | 35.8880723 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.442 | 0.129 | 1 | 92.0000000 | 28.7104579 | 15 | 68.0000000 | 68.9050989 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.084 | 0.126 | -1 | 88.0000000 | 89.0024194 | 15 | 81.0000000 | 71.7761447 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.056 | 0.126 | 1 | 91.0000000 | 114.8418315 | 15 | 86.0000000 | 43.0656868 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.080 | 0.126 | 1 | 89.0000000 | 60.2919615 | 15 | 84.5000000 | 48.8077784 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Reproductive Success | F | NO | Direct | Unstressed | -0.276 | 0.127 | 1 | 88.5000000 | 63.1630073 | 15 | 73.5000000 | 40.1946410 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.174 | 0.007 | 1 | 25.5400000 | 9.6994845 | 300 | 27.2600000 | 10.0458947 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.091 | 0.007 | 1 | 24.0500000 | 10.3923049 | 300 | 25.0300000 | 11.0851252 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.251 | 0.007 | 1 | 24.9300000 | 10.3923049 | 300 | 27.5900000 | 10.7387150 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | -0.390 | 0.007 | 1 | 42.3600000 | 16.8008928 | 300 | 36.3500000 | 13.8564065 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.485 | 0.007 | 1 | 33.3700000 | 18.5329436 | 300 | 43.3200000 | 22.3434554 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.227 | 0.007 | 1 | 38.4700000 | 23.2094808 | 300 | 43.8100000 | 23.7290961 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | 0.164 | 0.127 | 1 | 0.8200000 | 0.4880778 | 15 | 0.9000000 | 0.4593673 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | -0.548 | 0.131 | 1 | 0.9100000 | 0.2583941 | 15 | 0.7200000 | 0.4019464 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | -0.298 | 0.128 | 1 | 0.8800000 | 0.2583941 | 15 | 0.7600000 | 0.4880778 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | 0.007 | 0.007 | 1 | 22.2400000 | 10.0458947 | 300 | 22.3100000 | 10.2190998 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | -0.251 | 0.007 | 1 | 23.9000000 | 11.9511506 | 300 | 21.1700000 | 9.6994845 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | -0.070 | 0.007 | 1 | 24.2800000 | 10.7387150 | 300 | 23.5500000 | 10.2190998 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.124 | 0.007 | 1 | 24.2700000 | 10.2190998 | 300 | 25.5400000 | 9.6994845 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.140 | 0.007 | 1 | 22.7300000 | 8.8334591 | 300 | 24.0500000 | 10.3923049 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.095 | 0.007 | 1 | 24.0100000 | 9.1798693 | 300 | 24.9300000 | 10.3923049 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.210 | 0.007 | 1 | 38.2400000 | 22.3434554 | 300 | 42.3600000 | 16.8008928 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | -0.457 | 0.007 | -1 | 41.2900000 | 16.1080725 | 300 | 33.3700000 | 18.5329436 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | -0.202 | 0.007 | 1 | 42.9300000 | 20.6114046 | 300 | 38.4700000 | 23.2094808 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | -0.069 | 0.126 | 1 | 0.8600000 | 0.6316301 | 15 | 0.8200000 | 0.4880778 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | 0.040 | 0.126 | 1 | 0.9000000 | 0.2296837 | 15 | 0.9100000 | 0.2583941 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | -0.058 | 0.126 | 1 | 0.9200000 | 0.9187347 | 15 | 0.8800000 | 0.2583941 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | 0.061 | 0.007 | 1 | 21.6300000 | 10.0458947 | 300 | 22.2400000 | 10.0458947 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | 0.159 | 0.007 | 1 | 22.1700000 | 9.6994845 | 300 | 23.9000000 | 11.9511506 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | 0.141 | 0.007 | 1 | 22.7800000 | 10.5655099 | 300 | 24.2800000 | 10.7387150 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.292 | 0.007 | 1 | 24.2700000 | 10.2190998 | 300 | 27.2600000 | 10.0458947 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.232 | 0.007 | 1 | 22.7300000 | 8.8334591 | 300 | 25.0300000 | 11.0851252 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.359 | 0.007 | 1 | 24.0100000 | 9.1798693 | 300 | 27.5900000 | 10.7387150 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | -0.100 | 0.007 | 1 | 38.2400000 | 22.3434554 | 300 | 36.3500000 | 13.8564065 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.104 | 0.007 | 1 | 41.2900000 | 16.1080725 | 300 | 43.3200000 | 22.3434554 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | Lifespan | F | NO | Indirect | Unstressed | 0.041 | 0.007 | 1 | 42.9300000 | 20.6114046 | 300 | 43.8100000 | 23.7290961 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | 0.071 | 0.126 | 1 | 0.8600000 | 0.6316301 | 15 | 0.9000000 | 0.4593673 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | -0.537 | 0.131 | 1 | 0.9000000 | 0.2296837 | 15 | 0.7200000 | 0.4019464 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | Offspring Viability | F | NO | Direct | Unstressed | -0.211 | 0.127 | 1 | 0.9200000 | 0.9187347 | 15 | 0.7600000 | 0.4880778 | 15 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | 0.067 | 0.007 | 1 | 21.6300000 | 10.0458947 | 300 | 22.3100000 | 10.2190998 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | -0.103 | 0.007 | 1 | 22.1700000 | 9.6994845 | 300 | 21.1700000 | 9.6994845 | 300 | 3.719 |
| 69 | 26 | Wigby, S. and T. Chapman | 2004 | Wigby 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | Offspring Viability | M | NO | Direct | Unstressed | 0.074 | 0.007 | 1 | 22.7800000 | 10.5655099 | 300 | 23.5500000 | 10.2190998 | 300 | 3.719 |
Study ID: An ID given to the published paper the effect size is sourced from (n=65).
Group ID: An ID given to the research group that may have published several papers on the same species usuing the same or very similar experimental setup. [Was not use in analysis]
Species: The species used in the experimental evolution procedure (n = 15).
Taxon: The taxon to which the species belongs. One of the following: Beetle, fly, mouse, nematode, guppy, mite and cricket (taxa were selected arbitrarily based on the available data).
SS Strength, Ratios and SS Density’s (Column 7-9): Various ratios of the number of males to females and the total number of individuals kept together in an experiment [Was not used in any analysis]
Post cop and Pre cop: Whether a study allowed Pre/Post-copulatory sexual selection (1) or not (0).
Blinding: A binary classification, describing whether blind protocols were used during the experiment. Papers were assumed to be not blind unless declared otherwise.
Generations: The number of generations that the species was subject to differing levels of sexual selection, ranging from 1 to 162.
Enforced Monogamy: Whether the study had the low sexual selection treatment as enforced monogamy (YES) or not (NO). Not all studies compared enforced monogamy and SS+ treatments. Some used FB vs MB, where FB is the SS (low intensity).
n: Pooled sample size of the paired treatments.
Outcome: The fitness related outcome that was measured, e.g. fecundity, survival, or mating success (see Table S1 for all 20 categories). We applied our own classifications rather than relying on those provided by the authors, because different papers sometimes used different names for the same trait.
Outcome Class: To help guide analysis the outcomes were classed into three categories; ambiguous, indirect and direct (see Table S1).
Sex: A moderator variable with three levels, describing whether the effect size in question comes from a measurement of males (M), females (F), or individuals of both sexes (B).
Ambiguous: Is the fitness outcome ambiguous (YES) or not ambigous (NO). Ambiguous outcomes may be those that may not necessarily be directional, that is to say they may be a life history trait.
Environment: In the methods of the papers included in this study it was usually stated whether additional modifications to the experimental lines were made. Briefly, this was usually a modification that made conditions more stressful such as using a novel food source or elevated mutation load, the effect sizes from these experimental lines are labelled as ‘Stressed’. If it was clearly stated that there was no such modification it is labelled ‘Unstressed’. However, sometimes the paper was ambiguous in what lines had added stress or the results from stressed and unstressed lines were pooled together, in this case we label it as ‘Not Stated’.
g: Hedge’s g calculated using the compute.es package.
var.g: The within study variance associated with the effect size, g.
Positive Fitness: Whether the measurment used in the study is beneficial for fitness (1) or not (0). Note that g has already been multiplied by this column. We inverted all of the effect sizes pertaining to fitness outcomes that are expected to be negatively related to fitness by multiplying the effect size by -1.
mean/sd/n.low/high: The means, standard deviation and sample size for the low or high sexual selection treatments, used to calculate lnCVR (meta-analysis of variance). Rows without these values (NA) had hedges g’ derived from summary statistics (F, z, chi-square etc.).
JIF: Journal Impact factor at year of publication. Several impact factors were unable to be determined/found and are NA.We obtained the journal impact factor for each effect size at the time of publication using InCites Journal Citation Reports.
Tables of Sample Sizes
Here we present the number of effect sizes, publications, blind experiments, effect sizes in stresful conditions, male, female and both measures and different species used.
Table S4: Table of effect sizes included in our meta-analysis. See the text following the data table for an explanation of each column.
n.blind.ones <- (sum(prelim.data$Blind == "Blind"))
prelim.data %>%
summarise(
Effect_sizes_.Totalq = n(),
Publications = prelim.data$Study.ID %>% unique() %>% length(),
Blind_experiments = n.blind.ones,
Effect_sizes_.Enforced_monogamyq = (sum(prelim.data$Enforced.Monogamy == "YES")),
Effect_sizes_.Ambiguousq = (sum(prelim.data$Outcome.Class == "Ambiguous")),
Effect_sizes_.Indirectq = (sum(prelim.data$Outcome.Class == "Indirect")),
Effect_sizes_.Directq = (sum(prelim.data$Outcome.Class == "Direct")),
Effect_sizes_.Stressedq = (sum(prelim.data$Environment == "Stressed")),
Effect_sizes_.Unstressedq = (sum(prelim.data$Environment == "Unstressed")),
Effect_sizes_.Maleq = (sum(prelim.data$Sex == "M")),
Effect_sizes_.Femaleq = (sum(prelim.data$Sex == "F")),
Effect_sizes_.Both_sexesq = (sum(prelim.data$Sex == "B")),
Different_species = prelim.data$Species %>% unique() %>% length(),
Effect_sizes_.Beetleq = sum(Taxon == "Beetle"),
Effect_sizes_.Flyq = sum(Taxon == "Fly"),
Effect_sizes_.Mouseq = sum(Taxon == "Mouse"),
Effect_sizes_.Nematodeq = sum(Taxon == "Nematode"),
Effect_sizes_.Miteq = sum(Taxon == "Mite"),
Effect_sizes_.Cricketq = sum(Taxon == "Cricket"),
Effect_sizes_.Guppyq = sum(Taxon == "Guppy")) %>% melt() %>%
mutate(variable = gsub("_", " ", variable),
variable = gsub("[.]", "(", variable),
variable = gsub("q", ")", variable)) %>%
rename_("n" = "value", " " = "variable") %>%
pander(split.cell = 40, split.table = Inf)| n | |
|---|---|
| Effect sizes (Total) | 459 |
| Publications | 65 |
| Blind experiments | 54 |
| Effect sizes (Enforced monogamy) | 241 |
| Effect sizes (Ambiguous) | 96 |
| Effect sizes (Indirect) | 147 |
| Effect sizes (Direct) | 216 |
| Effect sizes (Stressed) | 94 |
| Effect sizes (Unstressed) | 335 |
| Effect sizes (Male) | 189 |
| Effect sizes (Female) | 219 |
| Effect sizes (Both sexes) | 51 |
| Different species | 15 |
| Effect sizes (Beetle) | 116 |
| Effect sizes (Fly) | 254 |
| Effect sizes (Mouse) | 40 |
| Effect sizes (Nematode) | 9 |
| Effect sizes (Mite) | 25 |
| Effect sizes (Cricket) | 6 |
| Effect sizes (Guppy) | 9 |
Table S5: Table of fitness outcomes included in our meta-analysis by sex.
Outcome_and_sex <- as.data.frame.matrix(table(prelim.data$Outcome, prelim.data$Sex))
colnames(Outcome_and_sex) <- cbind("Both", "Female", "Male")
Outcome_and_sex %>% pander(split.cell = 40, split.table = Inf)| Both | Female | Male | |
|---|---|---|---|
| Behavioural Plasticity | 0 | 2 | 0 |
| Body Condition | 0 | 0 | 1 |
| Body Size | 2 | 13 | 11 |
| Development Rate | 5 | 1 | 1 |
| Early Fecundity | 0 | 14 | 0 |
| Ejaculate Quality and Production | 0 | 0 | 23 |
| Extinction Rate | 4 | 0 | 0 |
| Fitness Senescence | 0 | 3 | 3 |
| Immunity | 5 | 15 | 15 |
| Lifespan | 0 | 35 | 3 |
| Male Attractiveness | 0 | 0 | 6 |
| Mating Duration | 0 | 1 | 9 |
| Mating Frequency | 0 | 6 | 5 |
| Mating Latency | 0 | 1 | 12 |
| Mating Success | 0 | 0 | 39 |
| Mutant Frequency | 6 | 0 | 2 |
| Offspring Viability | 15 | 26 | 15 |
| Pesticide Resistance | 2 | 0 | 0 |
| Reproductive Success | 12 | 102 | 42 |
| Strength | 0 | 0 | 2 |
Forest Plot
# Create new factor to order factors in a way where Ambig, Indirect and Direct are Grouped
prelim.data$Outcome_f = factor(prelim.data$Outcome, levels = c('Behavioural Plasticity', 'Body Size', 'Development Rate', 'Early Fecundity', 'Immunity', 'Mating Duration', 'Pesticide Resistance', 'Mutant Frequency', 'Body Condition', 'Fitness Senescence', 'Lifespan', 'Male Attractiveness', 'Mating Frequency', 'Mating Latency', 'Mating Success', 'Strength', 'Ejaculate Quality and Production', 'Extinction Rate', 'Offspring Viability', 'Reproductive Success'))
# define upper and lower bounds
prelim.data$lowerci <- prelim.data$g - 1.96*(sqrt(prelim.data$var.g))
prelim.data$upperci <- prelim.data$g + 1.96*(sqrt(prelim.data$var.g))
library(ggthemes)
#Generate a plot
p.meta <- prelim.data %>%
mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
Sex = replace(Sex, Sex == "M", "Male"),
Sex = replace(Sex, Sex == "F", "Female"),
Outcome.Class = factor(Outcome.Class, levels = c("Ambiguous", "Indirect", "Direct"))) %>%
ggplot(aes(y=reorder(AuthorYear, -g), x = g)) +
scale_color_manual(values = c("Ambiguous" = "#a50f15", "Indirect" = "#fe9929", "Direct" = "#4daf4a"),
name = "Relationship\nto fitness")+
scale_shape_manual(values=c(21,22,24))+
scale_fill_manual(values = c("Ambiguous" = "#a50f15", "Indirect" = "#fe9929", "Direct" = "#4daf4a"),
name = "Relationship\nto fitness")+
geom_errorbarh(aes(xmin = lowerci,
xmax = upperci,
color = Outcome.Class), height = 0.1, show.legend = FALSE) +
geom_point(aes(shape = Sex,
fill = Outcome.Class),
size = 1.75,
color = "grey20") +
scale_x_continuous(limits=c(-3.35, 12),
breaks = c(-3, -2, -1, 0, 1, 2, 3),
name=' Standardized Mean Difference (g) \n[positive values indicate sexual selection improves fitness components]') +
ylab('Reference') +
geom_vline(xintercept=0,
color='black',
linetype='dashed')+
facet_grid(Outcome_f~.,
labeller = label_wrap_gen(width=23),
scales= 'free',
space='free')+
guides(fill = guide_legend(override.aes = list(shape = 21, colour = "grey20", size = 6)),
shape = guide_legend(override.aes = list(size = 4.5)))+
#Add theme specifying text size, margins, lines etc.
theme_bw()+
theme(strip.text.y = element_text(angle = 0, size = 8, margin = margin(t=15, r=15, b=15, l=15)),
strip.background = element_rect(colour = NULL,
linetype = "blank",
fill = "gray90"),
text = element_text(size=11),
panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12,
face = "bold"),
axis.title.x = element_text(hjust = 0, size = 12))
### We can save the file in several vector graphics forms
#ggsave(plot = p.meta, filename = "figures/Big_forest_plot.eps", height = 18, width = 12)
# svg("figures/Big_forest_plot.svg", width=12, height=18)
# p.meta
# dev.off()
#
# pdf("figures/Big_forest_plot.pdf", width=12, height=18)
# p.meta
# dev.off()
p.meta
Figure S1: Forest plot of raw effect sizes and their 95% confidence intervals, grouped according to measured fitness components and the sex of the individuals whose fitness trait was measured (male, female, or both sexes mixed together). Rows with multiple data points denote studies that provided multiple effect sizes. Positive values indicate fitness benefits of sexual selection.
Meta-Analysis
Overall effect of sexual selection on fitness
We can obtain an overall weghted grand mean and confidence intervals with a simple intercept only for both Bayesian and REML models. Notably, in both models the estimates are approximately the same, with Bayesian estimates being marginally wider.
grand.mean.bayes <- brm(g | se(SE) ~ 1 #Note that running se(SE, sigma = TRUE) gives different result due to a difference in priors
+ (1|Study.ID)
+ (1|Outcome),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000, #Run 4 chains in parallel for 4000 iterations (2000 are burn in)
control = list(adapt_delta = 0.9999, max_treedepth = 15),
data = prelim.data %>% mutate(SE = sqrt(var.g)))grand.mean.bayes <- readRDS("data/grand.mean.bayes.rds")
#Run REML model
forest.model <- rma.mv(g, var.g,
mods = ~ 1,
random = list(~ 1 | Study.ID,
~ 1 | Outcome),
method = "REML",
data = prelim.data)
#Manually imput bayesian results
mean = c(forest.model$b, 0.22)
LCI = c(forest.model$ci.lb, 0.01)
UCI = c(forest.model$ci.ub, 0.43)
grand.means.df <- data.frame(rbind(mean, LCI, UCI))
colnames(grand.means.df) = c("REML", "Bayesian")
grand.means.df %>% pander(digits = 2)| REML | Bayesian | |
|---|---|---|
| mean | 0.22 | 0.22 |
| LCI | 0.03 | 0.01 |
| UCI | 0.41 | 0.43 |
Effect of Sexual Selection for different fitness components
Instead of running individual models for each fitness component we can run a model with the fitness components as predictors. In this case we maintain all of our fitness components and include study.id as a group level effect (to account for within study correlations in effect size). Using the brms package we can run a Bayesian model and generate fitted values for each fitness component.
model.fitness.components.bayes <- brm(g | se(SE) ~ Outcome #Note that running se(SE, sigma = TRUE) gives different result due to a difference in priors
+ (1|Study.ID),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000, #Run 4 chains in parallel for 4000 iterations (2000 are burn in)
control = list(adapt_delta = 0.999, max_treedepth = 15),
data = prelim.data %>% mutate(SE = sqrt(var.g)))
make_text_summary(model.fitness.components.bayes) %>% add_significance_stars() %>% tibble::rownames_to_column("Model Parameter") %>% pander(split.cell = 40, split.table = Inf)Table S6 Summary of model predictions for 20 fitness components. In the manuscript these values are presented as a text overlay on Figure 1 (Forest Plot).
components.brms <- readRDS(file = "data/components.brms.rds") #Avoid re-running model above
#Expand grid for environment and sex
brms.newdata <- as.data.frame(expand.grid(Outcome = unique(prelim.data$Outcome)))
#Get average SE: useful if using predict, but not fitted
av.se.g <- prelim.data %>% group_by(Outcome) %>% summarise(mean = mean(sqrt(var.g)))
brms.newdata$SE <- av.se.g$mean
#Add predictions/fitted values
brms.predict <- fitted(components.brms, newdata = brms.newdata, re_formula = NA)
brms.predict <- as.data.frame(brms.predict)
brms.predictions <- data.frame(brms.newdata$Outcome, brms.predict$Estimate, brms.predict$Est.Error, brms.predict$Q2.5, brms.predict$Q97.5)
#Name columns
colnames(brms.predictions) <- c("Fitness Component", "Estimate", "Error", "LCI", "UCI")
outcome.list.factor <- c('Behavioural Plasticity', 'Body Size', 'Development Rate', 'Early Fecundity', 'Immunity', 'Mating Duration', 'Pesticide Resistance', 'Mutant Frequency', 'Body Condition', 'Fitness Senescence', 'Lifespan', 'Male Attractiveness', 'Mating Frequency', 'Mating Latency', 'Mating Success', 'Strength', 'Ejaculate Quality and Production', 'Extinction Rate', 'Offspring Viability', 'Reproductive Success')
brms.predictions <- brms.predictions[match(outcome.list.factor, brms.predictions$`Fitness Component`),]
rownames(brms.predictions) <- NULL
sample.sizes.outcomes <- as.data.frame(table(prelim.data$Outcome))
colnames(sample.sizes.outcomes) <- c("Fitness Component", "n")
fitness.component.predictions <- left_join(brms.predictions, sample.sizes.outcomes, by = "Fitness Component")
fitness.component.predictions %>% add_significance_stars2() %>% pander(split.cell = 40, split.table = Inf, digits = 2)| Fitness Component | Estimate | Error | LCI | UCI | n | |
|---|---|---|---|---|---|---|
| Behavioural Plasticity | 0.13 | 0.17 | -0.2 | 0.46 | 2 | |
| Body Size | 0.36 | 0.071 | 0.22 | 0.5 | 26 | * |
| Development Rate | 0.53 | 0.12 | 0.28 | 0.76 | 7 | * |
| Early Fecundity | 0.31 | 0.13 | 0.049 | 0.56 | 14 | * |
| Immunity | -0.49 | 0.11 | -0.71 | -0.28 | 35 | * |
| Mating Duration | 0.16 | 0.082 | -0.011 | 0.32 | 10 | |
| Pesticide Resistance | 1.1 | 0.49 | 0.16 | 2.1 | 2 | * |
| Mutant Frequency | 0.27 | 0.34 | -0.4 | 0.97 | 8 | |
| Body Condition | -1.3 | 0.31 | -1.9 | -0.68 | 1 | * |
| Fitness Senescence | 0.68 | 0.076 | 0.54 | 0.83 | 6 | * |
| Lifespan | 0.24 | 0.068 | 0.1 | 0.37 | 38 | * |
| Male Attractiveness | 0.3 | 0.11 | 0.088 | 0.51 | 6 | * |
| Mating Frequency | 0.19 | 0.072 | 0.041 | 0.32 | 11 | * |
| Mating Latency | 0.69 | 0.073 | 0.54 | 0.83 | 13 | * |
| Mating Success | -0.082 | 0.071 | -0.22 | 0.052 | 39 | |
| Strength | 0.18 | 0.14 | -0.09 | 0.46 | 2 | |
| Ejaculate Quality and Production | 0.26 | 0.083 | 0.091 | 0.42 | 23 | * |
| Extinction Rate | 0.37 | 0.18 | 0.022 | 0.71 | 4 | * |
| Offspring Viability | 0.19 | 0.067 | 0.056 | 0.32 | 56 | * |
| Reproductive Success | 0.16 | 0.065 | 0.029 | 0.29 | 156 | * |
Models With Many Covariates
We collected data from fitness components that were deemed ambiguous as well as unambiguous. The ambiguous outcomes are likely to add in heterogeneity to the models and may not help us in answering questions of the fitness effects of sexual selection. A REML model utilising our complete dataset with many moderator variables would thus be:
model.preliminary <- rma.mv(g, var.g,
mods = ~ 1 + Sex * Environment + Taxon + Outcome.Class + log(Generations) + Blinding, # << ----- Add big model, then cull predictors to this one
random = list(~ 1 | Study.ID,
~ 1 | Outcome),
method = "REML",
data = prelim.data)
summary(model.preliminary, digits = 2)##
## Multivariate Meta-Analysis Model (k = 459; method: REML)
##
## logLik Deviance AIC BIC AICc
## -1706.78 3413.56 3455.56 3541.39 3457.77
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.23 0.48 65 no Study.ID
## sigma^2.2 0.14 0.37 20 no Outcome
##
## Test for Residual Heterogeneity:
## QE(df = 440) = 5609.94, p-val < .01
##
## Test of Moderators (coefficient(s) 2:19):
## QM(df = 18) = 49.38, p-val < .01
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.38 0.28 1.36 0.17 -0.17 0.93
## SexF 0.05 0.04 1.09 0.28 -0.04 0.14
## SexM 0.04 0.04 1.05 0.30 -0.04 0.13
## EnvironmentNot Stated 0.12 0.13 0.89 0.37 -0.14 0.37
## EnvironmentStressed 0.05 0.06 0.82 0.41 -0.06 0.16
## TaxonCricket 0.17 0.55 0.30 0.76 -0.91 1.25
## TaxonFly -0.26 0.16 -1.60 0.11 -0.57 0.06
## TaxonGuppy -0.35 0.50 -0.70 0.49 -1.34 0.64
## TaxonMite -0.08 0.25 -0.31 0.75 -0.56 0.41
## TaxonMouse -0.36 0.21 -1.74 0.08 -0.76 0.05 .
## TaxonNematode -0.27 0.51 -0.52 0.60 -1.26 0.73
## Outcome.ClassAmbiguous -0.00 0.20 -0.01 0.99 -0.39 0.39
## Outcome.ClassDirect 0.01 0.26 0.04 0.97 -0.49 0.51
## log(Generations) 0.01 0.05 0.12 0.90 -0.10 0.11
## BlindingNot Blind -0.07 0.21 -0.33 0.74 -0.49 0.35
## SexF:EnvironmentNot Stated 0.03 0.10 0.27 0.79 -0.18 0.23
## SexM:EnvironmentNot Stated 0.00 0.09 0.03 0.98 -0.17 0.18
## SexF:EnvironmentStressed 0.09 0.06 1.36 0.17 -0.04 0.21
## SexM:EnvironmentStressed -0.13 0.06 -2.07 0.04 -0.26 -0.01 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Here we can also run a Bayesian model alongside the REML model (metafor). The R2 for this model is 0.36 (95% CIs = 0.33-0.4).
#Also use bayesian model
brms.preliminary <- brm(g | se(SE) ~ 1 + Sex * Environment + log(Generations) + Blinding #Note that running se(SE, sigma = TRUE) gives different result due to a difference in priors
+ (1|Study.ID) #group level effects
+ (1|Outcome)
+ (1|Taxon),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000, #Run 4 chains in parallel for 4000 iterations (2000 are burn in)
control = list(adapt_delta = 0.999, max_treedepth = 15),
data = prelim.data %>% mutate(SE = sqrt(var.g)))
brms.preliminary <- readRDS(file = "data/brms.preliminary.rds") #Avoid re-running model above
#Plot model results
prelim.results.bayesplot <- bayesplot::mcmc_areas(posterior_samples(brms.preliminary)[,1:11]) +
geom_vline(xintercept = 0, linetype = 2) +
theme_bw()+
theme(panel.spacing = unit(0.1, "lines"),
text = element_text(size=16),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=16),
legend.title=element_text(size=16,
face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 14),
axis.title.y = element_text(size = 16, hjust = 0.35, margin = margin(r=-10)),
axis.text.y = element_text(angle = 0),
plot.title = element_text(size = 16))
prelim.results.bayesplot
make_text_summary(brms.preliminary) %>% add_significance_stars() %>% tibble::rownames_to_column("Model Parameter") %>% pander()| Model Parameter | Estimate | Est.Error | Q2.5 | Q97.5 | |
|---|---|---|---|---|---|
| b_Intercept | 0.271 | 0.242 | -0.214 | 0.758 | |
| b_SexF | 0.046 | 0.044 | -0.042 | 0.134 | |
| b_SexM | 0.044 | 0.043 | -0.04 | 0.129 | |
| b_EnvironmentNotStated | 0.105 | 0.127 | -0.141 | 0.361 | |
| b_EnvironmentStressed | 0.047 | 0.057 | -0.063 | 0.158 | |
| b_logGenerations | -0.005 | 0.048 | -0.098 | 0.092 | |
| b_BlindingNotBlind | -0.099 | 0.194 | -0.475 | 0.285 | |
| b_SexF:EnvironmentNotStated | 0.029 | 0.102 | -0.171 | 0.224 | |
| b_SexM:EnvironmentNotStated | 0.001 | 0.089 | -0.176 | 0.178 | |
| b_SexF:EnvironmentStressed | 0.084 | 0.062 | -0.035 | 0.205 | |
| b_SexM:EnvironmentStressed | -0.138 | 0.064 | -0.263 | -0.013 | * |
| sd_Outcome__Intercept | 0.366 | 0.084 | 0.236 | 0.564 | * |
| sd_Study.ID__Intercept | 0.476 | 0.053 | 0.385 | 0.588 | * |
| sd_Taxon__Intercept | 0.162 | 0.141 | 0.006 | 0.513 | * |
Figure S2 & Table S7: Bayesian model results for a preliminary model that explores many covariates collected in the dataset.
From these models we can see that there are several redundant moderators: Blinding and Generations show little effect and are not key to our research question (like sex and environment are). However, because taxa is a likely source of heterogeneity and effect size could reasonably be expected to differ between taxa, we investigate taxa as a fixed effect.
Sexual Selection Amongst Taxa
First we want to run the model using a restricted dataset where we remove effect sizes with Ambiguous outcomes (directionless or variable in their relation to fitness) or environments that were not stated whether they were stressed or unstressed (confusing and confounding). Notably, with the ambiguous measures included we already see a positive effect of sexual selection and thus removing these fitness components is not an attempt to obtain a significant answer, rather it restricts the data to traits that are less contentious and thus we believe this approach is conservative. In this model we use Sex, Environment, Taxon and the interaction between sex and environment.
#Restrict the dataset for unambiguous outcomes and environments
restricted.data <- prelim.data %>%
filter(Outcome.Class != "Ambiguous" & Environment != "Not Stated") %>%
mutate(Sex = as.character(Sex),
Environment = as.character(Environment),
Outcome.Class.2 = as.character(Outcome.Class),
Enforced.Monogamy = as.character(Enforced.Monogamy))
# Make sure the factors are leveled in the same order as we write our prediction function (below)
restricted.data$Environment <- restricted.data$Environment %>% factor() %>% relevel(ref="Unstressed")
restricted.data$Sex <- restricted.data$Sex %>% factor() %>% relevel(ref="M")
restricted.data$Outcome.Class <- restricted.data$Outcome.Class %>% factor() %>% relevel(ref="Indirect")
restricted.data$Taxon <- relevel(restricted.data$Taxon, ref = "Beetle")
restricted.data$Outcome <- restricted.data$Outcome %>% factor()
model.complete <- rma.mv(g, V = var.g,
mods = ~ 1 + Sex * Environment + Taxon, # << ----- Add big model, then cull predictors to this one
random = list(~ 1 | Study.ID,
~ 1 | Outcome),
method = "REML",
data = restricted.data)
summary(model.complete, digits = 2)##
## Multivariate Meta-Analysis Model (k = 336; method: REML)
##
## logLik Deviance AIC BIC AICc
## -1424.00 2848.00 2874.00 2923.19 2875.17
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.22 0.47 56 no Study.ID
## sigma^2.2 0.12 0.34 13 no Outcome
##
## Test for Residual Heterogeneity:
## QE(df = 325) = 4443.60, p-val < .01
##
## Test of Moderators (coefficient(s) 2:11):
## QM(df = 10) = 58.87, p-val < .01
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.35 0.17 2.05 0.04 0.02 0.68 *
## SexB -0.07 0.06 -1.03 0.30 -0.19 0.06
## SexF 0.05 0.03 1.92 0.06 -0.00 0.11 .
## EnvironmentStressed -0.12 0.04 -2.86 <.01 -0.20 -0.04 **
## TaxonCricket -0.02 0.50 -0.05 0.96 -1.01 0.96
## TaxonFly -0.18 0.17 -1.11 0.27 -0.51 0.14
## TaxonGuppy -0.21 0.50 -0.42 0.67 -1.18 0.76
## TaxonMite 0.02 0.24 0.07 0.95 -0.46 0.50
## TaxonMouse -0.33 0.24 -1.39 0.16 -0.79 0.14
## SexB:EnvironmentStressed 0.16 0.08 1.96 0.05 -0.00 0.32 .
## SexF:EnvironmentStressed 0.25 0.05 4.89 <.01 0.15 0.34 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The result is a model with estimates for various taxa, species, sexes and environments. To make sense of these estimates we should obtain average predictions for each moderator variable class of interest. We can do that by using a modified version version of a function used by Holman (2018). Here it alows us to cluster predictions for the different moderators of interest: Sex, environment, taxon etc. This is done by obtaining predictions using the base predict() function for the rma.mv() objects that have been previously created
# function that makes predict.rma work like a normal predict() function, instead of the idiosyncratic way that it works by default.
get.predictions.complete <- function(newdata){
B<-0; F<-0; Stressed<-0; Cricket<-0; Fly<-0; Guppy<-0; Mite<-0; Mouse<-0; interaction1<-0; interaction2<-0; interaction3<-0
if(newdata[1] == "B") B<-1
if(newdata[1] == "F") F<-1
if(newdata[2] == "Stressed") Stressed<-1
if(newdata[3] == "Cricket") Cricket<-1
if(newdata[3] == "Fly") Fly<-1
if(newdata[3] == "Guppy") Guppy<-1
if(newdata[3] == "Mite") Mite<-1
if(newdata[3] == "Mouse") Mouse<-1
if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1
predict(model.complete, newmods=c(B, F, Stressed, Cricket, Fly, Guppy, Mite, Mouse, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.complete <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed"),
Taxon = c("Beetle", "Cricket", "Fly", "Guppy", "Mite", "Mouse")))
predictions.complete <- cbind(predictions.complete, do.call("rbind", apply(predictions.complete, 1, get.predictions.complete))) %>%
select(Sex, Environment, Taxon, pred, se, ci.lb, ci.ub)
for(i in 4:7) predictions.complete[,i] <- unlist(predictions.complete[,i])
countpred = count_(restricted.data, c("Sex", "Environment", "Taxon"))
predictions.complete <- left_join(predictions.complete, countpred, by = c("Sex", "Environment", "Taxon"))
countpred = count_(restricted.data, c("Sex", "Environment", "Taxon"))
predictions.complete <- left_join(predictions.complete, countpred, by = c("Sex", "Environment", "Taxon"))
#Thirdly, plot the model predictions for effect size (Hedges' g) for male, female and both sexes under both stressed and unstressed condition and faceted for each taxon.
pd <- position_dodgev(0.6)
Taxon.metaanlysis <- predictions.complete %>%
mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
Sex = replace(Sex, Sex == "M", "Male"),
Sex = replace(Sex, Sex == "F", "Female"),
Environment = replace(as.character(Environment), Environment == "Stressed", "Stressful"),
Environment = replace(Environment, Environment == "Unstressed", "Benign"),
Sex = factor(Sex, levels = c("Male", "Both", "Female"))) %>%
ggplot(aes(x = pred, y= Environment, fill = Sex)) +
geom_vline(xintercept = 0, linetype = 2, colour = "black") +
geom_errorbarh(aes(xmin = predictions.complete$ci.lb,
xmax = predictions.complete$ci.ub,
color= Sex),
height = 0, position = pd, show.legend = F) +
geom_point(position = pd, size=2, shape=21, color = "grey20") +
facet_grid(Taxon ~.)+
ylab("Environment \n")+
xlab("\nModel Prediction (Hedges g)")+
xlim(-1, 2)+
ggtitle('Effects of Sex and Stress on \nPopulation Fitness for Each Taxon')+
scale_fill_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
scale_color_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
guides(fill = guide_legend(reverse=T, override.aes = list(size = 4.5)))+
theme_bw()+
theme(strip.text.y = element_text(angle = 0, size = 14, margin = margin(r=20, l=20)),
strip.background = element_rect(colour = NULL,
linetype = "blank",
fill = "gray90"),
text = element_text(size=14),
panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=14),
legend.title=element_text(size=14,
face = "bold"),
axis.title.x = element_text(hjust = 0.3, size = 14),
axis.title.y = element_text(size = 14),
plot.title = element_text(size = 14))
#ggsave(plot = Taxon.metaanlysis, filename = "figures/Taxon_metaanalysis_plot.svg", height = 8, width = 8)
Taxon.metaanlysis
Figure S3: The predictions from this model indicate some heterogeneity between taxon. However, the most apparent difference between taxa is that confidence bands increase for taxa with low sample size. As previously shown, the beetle and fly taxa are the most heavily sampled and in the above figure have the narrowest confidence bands. Importantly, the overall direction of effect does not change between taxon, although guppies and mice show near zero effect sizes. Here we see that under stressed environments, females from all taxa appear to have greater fitness increase than males or ‘both’.
Table S8 The predictions for the above figure looking at the effect of sexual selection amongst taxa uses the following dataframe.
colnames(predictions.complete) <- c("Sex", "Environment", "Taxon", "Prediction", "SE", "CI.lb", "CI.ub", "n")
predictions.complete <- format(predictions.complete, digits = 2)
predictions.complete[[9]] <- NULL
predictions.complete %>% pander()| Sex | Environment | Taxon | Prediction | SE | CI.lb | CI.ub | n |
|---|---|---|---|---|---|---|---|
| M | Unstressed | Beetle | 0.3462 | 0.17 | 0.015 | 0.68 | 31 |
| B | Unstressed | Beetle | 0.2804 | 0.18 | -0.065 | 0.63 | 2 |
| F | Unstressed | Beetle | 0.4006 | 0.17 | 0.068 | 0.73 | 15 |
| M | Stressed | Beetle | 0.2279 | 0.17 | -0.109 | 0.57 | 2 |
| B | Stressed | Beetle | 0.3229 | 0.18 | -0.027 | 0.67 | 6 |
| F | Stressed | Beetle | 0.5277 | 0.17 | 0.193 | 0.86 | 9 |
| M | Unstressed | Cricket | 0.3212 | 0.49 | -0.648 | 1.29 | 1 |
| B | Unstressed | Cricket | 0.2554 | 0.50 | -0.719 | 1.23 | NA |
| F | Unstressed | Cricket | 0.3756 | 0.50 | -0.595 | 1.35 | NA |
| M | Stressed | Cricket | 0.2030 | 0.50 | -0.769 | 1.18 | NA |
| B | Stressed | Cricket | 0.2980 | 0.50 | -0.681 | 1.28 | NA |
| F | Stressed | Cricket | 0.5028 | 0.50 | -0.469 | 1.47 | NA |
| M | Unstressed | Fly | 0.1618 | 0.14 | -0.105 | 0.43 | 73 |
| B | Unstressed | Fly | 0.0960 | 0.14 | -0.186 | 0.38 | 9 |
| F | Unstressed | Fly | 0.2162 | 0.14 | -0.053 | 0.48 | 93 |
| M | Stressed | Fly | 0.0436 | 0.14 | -0.230 | 0.32 | 13 |
| B | Stressed | Fly | 0.1386 | 0.15 | -0.154 | 0.43 | 8 |
| F | Stressed | Fly | 0.3434 | 0.14 | 0.073 | 0.61 | 19 |
| M | Unstressed | Guppy | 0.1372 | 0.48 | -0.813 | 1.09 | 4 |
| B | Unstressed | Guppy | 0.0715 | 0.49 | -0.889 | 1.03 | NA |
| F | Unstressed | Guppy | 0.1916 | 0.48 | -0.758 | 1.14 | 3 |
| M | Stressed | Guppy | 0.0190 | 0.49 | -0.933 | 0.97 | NA |
| B | Stressed | Guppy | 0.1140 | 0.49 | -0.848 | 1.08 | NA |
| F | Stressed | Guppy | 0.3188 | 0.49 | -0.633 | 1.27 | NA |
| M | Unstressed | Mite | 0.3626 | 0.23 | -0.082 | 0.81 | 4 |
| B | Unstressed | Mite | 0.2968 | 0.23 | -0.160 | 0.75 | 2 |
| F | Unstressed | Mite | 0.4170 | 0.23 | -0.028 | 0.86 | 7 |
| M | Stressed | Mite | 0.2444 | 0.23 | -0.204 | 0.69 | 1 |
| B | Stressed | Mite | 0.3394 | 0.23 | -0.118 | 0.80 | 4 |
| F | Stressed | Mite | 0.5442 | 0.23 | 0.098 | 0.99 | 5 |
| M | Unstressed | Mouse | 0.0165 | 0.22 | -0.411 | 0.44 | 6 |
| B | Unstressed | Mouse | -0.0492 | 0.23 | -0.492 | 0.39 | 2 |
| F | Unstressed | Mouse | 0.0709 | 0.22 | -0.358 | 0.50 | 5 |
| M | Stressed | Mouse | -0.1017 | 0.22 | -0.531 | 0.33 | 7 |
| B | Stressed | Mouse | -0.0067 | 0.23 | -0.456 | 0.44 | NA |
| F | Stressed | Mouse | 0.1981 | 0.22 | -0.232 | 0.63 | 5 |
Given that none of categories of taxa significantly impact effect size, and many categories are unsamples (Table S8) we can incorporate them as random/group level effects in models henceforth.
REML: Effects of Environment and Sex
Here we ask two key questions: Does sexual selection benefit populations in stressful environments more than benign environments? AND Do the benefits of sexual selection differ between the sexes?
#We can run a model where the outcome is crossed with study (Study.ID) and Taxon as random effects and environment, sex and their interactions are fixed effects.
model.complete2 <- rma.mv(g, V = var.g,
mods = ~ 1 + Sex * Environment,
random = list(~ 1 | Study.ID,
~ 1 | Outcome,
~ 1 | Taxon),
method = "REML",
data = restricted.data)
summary(model.complete2, digits = 2)##
## Multivariate Meta-Analysis Model (k = 336; method: REML)
##
## logLik Deviance AIC BIC AICc
## -1429.57 2859.14 2877.14 2911.33 2877.70
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.20 0.45 56 no Study.ID
## sigma^2.2 0.11 0.34 13 no Outcome
## sigma^2.3 0.00 0.00 6 no Taxon
##
## Test for Residual Heterogeneity:
## QE(df = 330) = 4576.52, p-val < .01
##
## Test of Moderators (coefficient(s) 2:6):
## QM(df = 5) = 55.94, p-val < .01
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.21 0.12 1.82 0.07 -0.02 0.44 .
## SexB -0.06 0.06 -1.02 0.31 -0.19 0.06
## SexF 0.05 0.03 1.90 0.06 -0.00 0.11 .
## EnvironmentStressed -0.12 0.04 -2.94 <.01 -0.20 -0.04 **
## SexB:EnvironmentStressed 0.17 0.08 2.06 0.04 0.01 0.33 *
## SexF:EnvironmentStressed 0.25 0.05 4.95 <.01 0.15 0.35 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1We conducted hypothesis tests on the above model to investigate the effective difference between two groups. Depending on the package this method is also refered to as linear hypothesis or anova (metafor).
Table S9 Using the anova.rma function we can conduct hypothesis tests between two categorical groups in the model. Here we conduct 5 tests testing relative effects of sexual selection between the sexes and in different environments.
#anova where you specify the values based on the list of moderators
anova.1 = anova(model.complete2, L=c(0, 0, -1, 0, 0, 0))
anova.2 = anova(model.complete2, L=c(0, 0, -1, 0, 0, -1))
anova.3 = anova(model.complete2, L=c(0, 0, 0, -1, 0, -1))
anova.4 = anova(model.complete2, L=c(0, 0, 0, -1, 0, 0))
anova.5 = anova(model.complete2, L=c(0, 0, 0, -1, -1, 0))
anova.list <- list(anova.1, anova.2, anova.3, anova.4, anova.5)
anova.frame <- t(data.frame(lapply(anova.list, function(x) {
data.frame(x[["hyp"]],
x[["Lb"]],
x[["se"]],
x[["Lb"]] - 1.96*x[["se"]],
x[["Lb"]] + 1.96*x[["se"]],
x[["pval"]])
})))
anova.frame <- as.data.frame(split(anova.frame, rep(1:6)))
colnames(anova.frame) <- c("Hypothesis", "Estimate", "Est.Error", "CI.Lower", "CI.Upper", "pval")
anova.frame$Estimate <- as.numeric(levels(anova.frame$Estimate))[anova.frame$Estimate]
anova.frame$Est.Error <- as.numeric(levels(anova.frame$Est.Error))[anova.frame$Est.Error]
anova.frame$CI.Lower <- as.numeric(levels(anova.frame$CI.Lower))[anova.frame$CI.Lower]
anova.frame$CI.Upper <- as.numeric(levels(anova.frame$CI.Upper))[anova.frame$CI.Upper]
anova.frame$pval <- as.numeric(levels(anova.frame$pval))[anova.frame$pval]
anova.frame <- format(anova.frame, digits = 2)
anova.frame$star <- c("", "*", "*", "*", "")
colnames(anova.frame)[colnames(anova.frame)=="star"] <- " "
anova.frame$pval <- NULL
rownames(anova.frame) <- c("M vs F, Benign", "M vs F, Stressful", "Benign vs Stressful, Female", "Benign vs Stressful, Male", "Benign vs Stressful, Both")
anova.frame %>% pander(split.table = Inf)| Hypothesis | Estimate | Est.Error | CI.Lower | CI.Upper | ||
|---|---|---|---|---|---|---|
| M vs F, Benign | -SexF = 0 | -0.054 | 0.028 | -0.11 | 0.0016 | |
| M vs F, Stressful | -SexF - SexF:EnvironmentStressed = 0 | -0.302 | 0.044 | -0.39 | -0.2161 | * |
| Benign vs Stressful, Female | -EnvironmentStressed - SexF:EnvironmentStressed = 0 | -0.127 | 0.037 | -0.20 | -0.0545 | * |
| Benign vs Stressful, Male | -EnvironmentStressed = 0 | 0.121 | 0.041 | 0.04 | 0.2020 | * |
| Benign vs Stressful, Both | -EnvironmentStressed - SexB:EnvironmentStressed = 0 | -0.047 | 0.074 | -0.19 | 0.0970 |
From these anovas we see that a stressful environment leads to a significantly greater increase in fitness components for “female”" but NOT “both” sexes, while significantly decreasing male fitness. We also see there is a difference between the sexes in stressful environments (females with larger mean) but not in benign environments (marginal).
Bayesian: Effects of Environment and Sex
We can run a Bayesian model using the brms package. The model has the same moderators as the REML approach used above. The brms package sets standard priors that are selected to be ‘weakly informative’. The R2 of this Bayesian model was 0.35 (95% CIs = 0.31-0.39). Additionally, we can obtain the distribution of posterior fitted values and examine the model summary
#Use brms to create a model similar to the one used in the REML approach.
brms.complete2 <- brm(g | se(SE) ~ 1 + Sex * Environment #se(SE, sigma = TRUE) gives differnt results
+ (1|Taxon) #group level effect #1
+ (1|Study.ID) #group level effect #2
+ (1|Outcome), #group level effect #3
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000, # Run 4 chains in parallel for 4000 iterations (2000 are burn in)
control = list(adapt_delta = 0.9999, max_treedepth = 15),
data = restricted.data %>% mutate(SE = sqrt(var.g)))Table S10 Model estimate summary table for the Bayesian model investigating the effect of environment and sex (alongside sexual selection) on fitness.
brms.complete2 <- readRDS(file = "data/brms.complete2.rds") #Avoid re-running model above
#You can obtain a posterior sampling through the ``fitted.brmsfit`` function, which gives the same posterior values as doing it manually (see below).**Note that the fitted values obtained here have much smaller error than the predict values**
# #obtain average variance of each group in the prediction
# av.var.g <- as.numeric(c(restricted.data %>% filter (Sex == 'M' & Environment == "Unstressed") %>% summarise(mean = mean(var.g)),
# restricted.data %>% filter (Sex == 'B' & Environment == "Unstressed") %>% summarise(mean = mean(var.g)),
# restricted.data %>% filter (Sex == 'F' & Environment == "Unstressed") %>% summarise(mean = mean(var.g)),
# restricted.data %>% filter (Sex == 'M' & Environment == "Stressed") %>% summarise(mean = mean(var.g)),
# restricted.data %>% filter (Sex == 'B' & Environment == "Stressed") %>% summarise(mean = mean(var.g)),
# restricted.data %>% filter (Sex == 'F' & Environment == "Stressed") %>% summarise(mean = mean(var.g))))
#Expand grid for environment and sex
# brms.newdata <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
# Environment = c("Unstressed", "Stressed")))
# #Add variance
# brms.newdata$var.g <- av.var.g
#
# #Add predictions
# brms.predict <- fitted(meta.brms, newdata = brms.newdata, re_formula = NA)
# brms.predict <- as.data.frame(brms.predict)
# brms.predictions <- data.frame(brms.newdata$Sex, brms.newdata$Environment, brms.predict$Estimate, brms.predict$Est.Error, brms.predict$Q2.5, brms.predict$Q97.5)
#
# #Name columns
# colnames(brms.predictions) <- c("Sex", "Environment", "Estimate", "Error", "LCI", "UCI")
#Alternatively you can obtain posterior samples manually.
post <- (posterior_samples(brms.complete2,
pars = c("b_Intercept", "b_SexB", "b_SexF",
"b_EnvironmentStressed", "b_SexB:EnvironmentStressed",
"b_SexF:EnvironmentStressed")) %>%
mutate(both_benign = b_Intercept + b_SexB,
both_stressful = b_Intercept + b_SexB + b_EnvironmentStressed + `b_SexB:EnvironmentStressed`,
male_benign = b_Intercept,
male_stressful = b_Intercept + b_EnvironmentStressed,
female_benign = b_Intercept + b_SexF,
female_stressful = b_Intercept + b_SexF + b_EnvironmentStressed + `b_SexF:EnvironmentStressed`))[,-(1:6)]
#Obtain posterior fitted values and transform
# posterior_fit <- data.frame(t(posterior_linpred(meta.brms, newdata = brms.newdata, re_formula = NA)))
#Add columns for Environment and Sex
post <- as.data.frame(t(post))
post$Sex <- c("Both", "Both", "Male", "Male", "Female", "Female")
post$Environment <- c("Benign", "Stressful", "Benign", "Stressful", "Benign", "Stressful")
#Clean up dataframe
post <- melt(post, id = c("Sex", "Environment"))
post$variable <- NULL
make_text_summary(brms.complete2) %>% add_significance_stars() %>% tibble::rownames_to_column("Model Parameter") %>% pander()| Model Parameter | Estimate | Est.Error | Q2.5 | Q97.5 | |
|---|---|---|---|---|---|
| b_Intercept | 0.222 | 0.165 | -0.104 | 0.546 | |
| b_SexB | -0.063 | 0.064 | -0.186 | 0.061 | |
| b_SexF | 0.054 | 0.028 | 0 | 0.11 | |
| b_EnvironmentStressed | -0.12 | 0.04 | -0.198 | -0.039 | * |
| b_SexB:EnvironmentStressed | 0.164 | 0.082 | 0.003 | 0.322 | * |
| b_SexF:EnvironmentStressed | 0.247 | 0.049 | 0.151 | 0.342 | * |
| sd_Outcome__Intercept | 0.393 | 0.117 | 0.225 | 0.675 | * |
| sd_Study.ID__Intercept | 0.463 | 0.054 | 0.37 | 0.579 | * |
| sd_Taxon__Intercept | 0.145 | 0.139 | 0.006 | 0.517 | * |
Like with the REML approach, from these model results we can test several hypotheses between the categories of environment and sex.
Table S11 Hypothesis tests for the Bayesian model are similar to the REML model (Table S9), with slight differences to CIs.
#Obtain hypothesis estimates
brms.hypothesis <- hypothesis(brms.complete2, c("0 = SexF",
"0 = SexF + SexF:EnvironmentStressed",
"0 = SexF:EnvironmentStressed + EnvironmentStressed",
"0 = EnvironmentStressed",
"0 = SexB:EnvironmentStressed + EnvironmentStressed"))
#Format into dataframe
brms.hypothesis.table <-
data.frame(brms.hypothesis[["hypothesis"]][["Hypothesis"]],
brms.hypothesis[["hypothesis"]][["Estimate"]],
brms.hypothesis[["hypothesis"]][["Est.Error"]],
brms.hypothesis[["hypothesis"]][["CI.Lower"]],
brms.hypothesis[["hypothesis"]][["CI.Upper"]],
brms.hypothesis[["hypothesis"]][["Star"]])
colnames(brms.hypothesis.table) <- c("Hypothesis", "Estimate", "Est.Error", "CI.Lower", "CI.Upper", " ")
brms.hypothesis.table <- format(brms.hypothesis.table, digits = 2)
rownames(brms.hypothesis.table) <- c("M vs F, Benign", "M vs F, Stressful", "Benign vs Stressful, Female", "Benign vs Stressful, Male", "Benign vs Stressful, Both")
brms.hypothesis.table %>% pander(split.table = Inf)| Hypothesis | Estimate | Est.Error | CI.Lower | CI.Upper | ||
|---|---|---|---|---|---|---|
| M vs F, Benign | (0)-(SexF) = 0 | -0.054 | 0.028 | -0.110 | -0.00033 | * |
| M vs F, Stressful | (0)-(SexF+SexF:EnvironmentStressed) = 0 | -0.301 | 0.043 | -0.384 | -0.21661 | * |
| Benign vs Stressful, Female | (0)-(SexF:EnvironmentStressed+EnvironmentStressed) = 0 | -0.127 | 0.037 | -0.199 | -0.05499 | * |
| Benign vs Stressful, Male | (0)-(EnvironmentStressed) = 0 | 0.120 | 0.040 | 0.039 | 0.19776 | * |
| Benign vs Stressful, Both | (0)-(SexB:EnvironmentStressed+EnvironmentStressed) = 0 | -0.044 | 0.074 | -0.188 | 0.10162 |
Using predictions from both REML and Bayesian models we can obtain a figure that plots the mean/median predictions as well as distribution density (Bayesian) and 95 % CI (REML).
#Generate predictions without taxon utilising the previously described function
get.predictions.complete2 <- function(newdata){
B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
if(newdata[1] == "B") B<-1
if(newdata[1] == "F") F<-1
if(newdata[2] == "Stressed") Stressed<-1
if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1
predict(model.complete2, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.complete2 <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed")))
predictions.complete2 <- cbind(predictions.complete2, do.call("rbind", apply(predictions.complete2, 1, get.predictions.complete2))) %>%
select(Sex, Environment, pred, se, ci.lb, ci.ub)
for(i in 3:6) predictions.complete2[,i] <- unlist(predictions.complete2[,i])
countpred <- count_(restricted.data, c("Sex", "Environment"))
predictions.complete2 <- left_join(predictions.complete2, countpred, by = c("Sex", "Environment"))
colnames(predictions.complete2) <- c("Sex", "Environment", "Prediction", "SE", "CI.lb", "CI.ub", "n")
predictions.complete2 <- predictions.complete2 %>%
mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
Sex = replace(Sex, Sex == "M", "Male"),
Sex = replace(Sex, Sex == "F", "Female"),
Environment = replace(as.character(Environment), Environment == "Stressed", "Stressful"),
Environment = replace(Environment, Environment == "Unstressed", "Benign"),
Sex = factor(Sex, levels = c("Male", "Both", "Female")))
#Plot the posterior values from the Bayesian model as density ridges
pd <- position_dodgev(height = 0.3)
posterior.plot <- post %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))) %>% ggplot()+
stat_density_ridges(aes(x=value, y = Environment, fill = Sex), alpha = 0.65, scale = 0.6, position = position_nudge(y = 0.15), height = 10, show.legend = F, quantile_lines = T, quantiles = 2)+
geom_vline(xintercept = 0, linetype = 2, colour = "black") +
ylab("Environment")+
xlab("\nEffect Size (Hedges' g)")+
scale_fill_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
scale_color_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
scale_x_continuous(limits = c(-0.75, 1.5), breaks = c(-1, -.5, 0, 0.5, 1, 1.5))+
theme_bw()+
theme(panel.spacing = unit(0.1, "lines"),
text = element_text(size=16),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=16),
legend.title=element_text(size=16,
face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 14),
axis.title.y = element_text(size = 16, hjust = 0.35, margin = margin(r=-10)),
axis.text.y = element_text(angle = 0),
plot.title = element_text(size = 16))
#Add the REML predictions as circles with error bars
both.plots <- posterior.plot +
geom_errorbarh(data = predictions.complete2 %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(x= Prediction, xmin = predictions.complete2$CI.lb,
xmax = predictions.complete2$CI.ub, y = Environment,
color = Sex),
height = 0, show.legend = F, position = pd)+
geom_point(data = predictions.complete2 %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(x = Prediction, y = Environment, size=n, fill = Sex),
shape=21, color = "grey20", position = pd) +
guides(fill = guide_legend(reverse=T, override.aes = list(size = 7.5)))+
scale_size(guide = 'none')+
scale_y_discrete(expand=c(0.075,0))
# svg("figures/both_plot.svg", width=6, height=6)
# both.plots
# dev.off()
# #
# pdf("figures/both_plot.pdf", width = 6, height = 6)
# both.plots
# dev.off()
both.plots
Figure S3: Sexual selection generally increases population fitness, especially for females under stressful conditions. The benefits of sexual selection on fitness for females under stressful conditions are small-medium according to Cohen’s interperetation of effect sizes. Circle size is proportional to sample size (shown below). The REML predictions are shown as circles with error bars and the Bayesian predictions as density ridges. This figure can also be found in the main manuscript.
Table S12 The REML predictions in the plot above use the following dataframe
predictions.complete2 <- format(predictions.complete2, digits = 2)
predictions.complete2$Prediction = as.numeric(predictions.complete2$Prediction)
predictions.complete2$CI.lb = as.numeric(predictions.complete2$CI.lb)
predictions.complete2$CI.ub = as.numeric(predictions.complete2$CI.ub)
predictions.complete2$n = as.numeric(predictions.complete2$n)
predictions.complete2 %>% pander()| Sex | Environment | Prediction | SE | CI.lb | CI.ub | n |
|---|---|---|---|---|---|---|
| Male | Benign | 0.213 | 0.12 | -0.017 | 0.44 | 119 |
| Both | Benign | 0.149 | 0.13 | -0.101 | 0.4 | 15 |
| Female | Benign | 0.267 | 0.12 | 0.035 | 0.5 | 123 |
| Male | Stressful | 0.092 | 0.12 | -0.145 | 0.33 | 23 |
| Both | Stressful | 0.197 | 0.13 | -0.063 | 0.46 | 18 |
| Female | Stressful | 0.394 | 0.12 | 0.159 | 0.63 | 38 |
Estimating Hedges’ g Heterogeneity Using I2
Using an adapted function from https://github.com/daniel1noble/metaAidR we can obtain confidence intervals for total I2 and the individual components of the random effects. There are different methods to obtain estimates of I2. Here we obtain an overall value of I2 that is weighted based on variance and where estimates of heterogeneity are sourced from sigma2 of the respective models. The values are based on the REML models.
I2(model.complete2, restricted.data$var.g) %>% pander(digits = 3)| I2_Est. | 2.5% CI | 97.5% CI | |
|---|---|---|---|
| Study.ID | 61.5 | 42.9 | 78.6 |
| Outcome | 33.5 | 15.8 | 52.7 |
| Taxon | 0 | 0 | 0 |
| total | 94.9 | 93 | 96.4 |
These values indicate that 61.5 % of total heterogeneity is due to the between study heterogeneity and 33.5 % for between outcome heterogeneity between different outcomes. The total I2 is 95 %, a reasonably high I2 value. However this is relatively common in Ecology and Evolution total.
Meta-Analysis on Variance
Obtaining lnCVR and Meta-Analysis Models
This meta-analysis on variation utilises previously described and utilised methods devoleped (Nakagawa et al. 2015; Senior et al. 2016). Our goal is to determine whether the phenotypic variance in fitness related traits is impacted by sexual selection. We would assume that if selection is occuring not only would the trait mean shift in a certain direction but the variance associated with those changes to the mean would also decrease. In this case we use an effect size statistic known as the natural log of the coefficient of variation ratio (lnCVR).
# Firstly, we setup our calculation by creating a a restricted dataset with only unabmiguous fitness outcomes and running the functions developed by Nakagawa et al. 2015:
Calc.lnCVR<-function(CMean, CSD, CN, EMean, ESD, EN){
ES<-log(ESD) - log(EMean) + 1 / (2*(EN - 1)) - (log(CSD) - log(CMean) + 1 / (2*(CN - 1)))
return(ES)
}
#for variance of lnCVR
Calc.var.lnCVR<-function(CMean, CSD, CN, EMean, ESD, EN, Equal.E.C.Corr=T){
if(Equal.E.C.Corr==T){
mvcorr<-cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$estimate
S2<- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * mvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * mvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
}
else{
Cmvcorr<-cor.test(log(CMean), log(CSD))$estimate
Emvcorr<-cor.test(log(EMean), (ESD))$estimate
S2<- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * Cmvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * Emvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
}
return(S2)
}
# Secondly, we utilise those formulas to obtain lnCVR and var.CVR for all applicable effect sizes. Noting that not all of the dataset has means, SD and n; some were calculated from summary statistics and are not able to have lnCVR calculated:
#Calculate lnCVr and var.lnCVr
#for lnCVR
restricted.data$lnCVr <- Calc.lnCVR(restricted.data$mean.low, restricted.data$sd.low, restricted.data$n.low, restricted.data$mean.high, restricted.data$sd.high, restricted.data$n.high)
#for variance in lnCVR
restricted.data$var.lnCVr <- Calc.var.lnCVR(restricted.data$mean.low, restricted.data$sd.low, restricted.data$n.low, restricted.data$mean.high, restricted.data$sd.high, restricted.data$n.high, Equal.E.C.Corr=T) #Equal.E.C.Corr = T assumes that the correlaiton between mean and sd (Taylor's Law) is equal for the mean and control groups
restricted.data2 <- restricted.data Although not previously done extensively, it seems that the best way to conduct this analysis is not through subsetting but through utilising model predictions as we did with Hedges’ g previously, that way we retain the same methodology in model structure and test the same hypotheses. This can be done be utilising the same predict function but for lnCVR and var.lnCVR. For the brms model we can obtain predictions manually or through the fitted.brmsfit.
Multilevel-model using lnCVR and REML:
variance.model <- rma.mv(lnCVr, V = var.lnCVr, mods = ~ 1 + Sex*Environment,
random = list(~ 1 | Taxon,
~ 1 | Study.ID,
~ 1 | Outcome),
method = "REML", data = restricted.data2)
summary(variance.model, digits = 2)##
## Multivariate Meta-Analysis Model (k = 277; method: REML)
##
## logLik Deviance AIC BIC AICc
## -6796.24 13592.48 13610.48 13642.90 13611.17
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.06 0.25 6 no Taxon
## sigma^2.2 0.15 0.38 46 no Study.ID
## sigma^2.3 0.15 0.38 11 no Outcome
##
## Test for Residual Heterogeneity:
## QE(df = 271) = 19525.52, p-val < .01
##
## Test of Moderators (coefficient(s) 2:6):
## QM(df = 5) = 3845.00, p-val < .01
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.07 0.18 0.41 0.68 -0.28 0.42
## SexB -0.05 0.04 -1.33 0.18 -0.12 0.02
## SexF -0.15 0.01 -11.40 <.01 -0.18 -0.13 ***
## EnvironmentStressed -0.21 0.02 -10.53 <.01 -0.25 -0.17 ***
## SexB:EnvironmentStressed -0.25 0.04 -5.94 <.01 -0.34 -0.17 ***
## SexF:EnvironmentStressed -0.75 0.02 -31.12 <.01 -0.80 -0.70 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Again, we use brms to obtain Bayesian model estimates. For this model the R2 is 0.34 (95% CIs = 0.32-0.36).
#When knitting this markdown this model stalls
#Use brm to create a model similar to the one used in the REML approach.
variance.brms <- brm(lnCVr| se(SE.v) ~ 1 + Sex * Environment #response is Hedges' g and the standard error associated with it (SE), sex, environment and their interaction are moderators
+ (1|Taxon) #group level effects
+ (1|Study.ID)
+ (1|Outcome),
family = "gaussian", #default
seed = 1,
cores = 4, chains = 4, iter = 4000, #Run 4 chains in parrallel for 4000 iterations (2000 are burn in)
control = list(adapt_delta = 0.999, max_treedepth = 15),
data = restricted.data2 %>% mutate(SE.v = sqrt(var.lnCVr)))Table S13 Model estimates, including random effect sigma value for the model of phenotypic variance (lnCVR)
var.brms <- readRDS(file = "data/variance.brms.rds") #Avoid re-running model above
post.variance <- (posterior_samples(var.brms,
pars = c("b_Intercept", "b_SexB", "b_SexF",
"b_EnvironmentStressed", "b_SexB:EnvironmentStressed",
"b_SexF:EnvironmentStressed")) %>%
mutate(both_benign = b_Intercept + b_SexB,
both_stressful = b_Intercept + b_SexB + b_EnvironmentStressed + `b_SexB:EnvironmentStressed`,
male_benign = b_Intercept,
male_stressful = b_Intercept + b_EnvironmentStressed,
female_benign = b_Intercept + b_SexF,
female_stressful = b_Intercept + b_SexF + b_EnvironmentStressed + `b_SexF:EnvironmentStressed`))[,-(1:6)]
#Add columns for Environment and Sex
post.variance <- as.data.frame(t(post.variance))
post.variance$Sex <- c("Both", "Both", "Male", "Male", "Female", "Female")
post.variance$Environment <- c("Benign", "Stressful", "Benign", "Stressful", "Benign", "Stressful")
#Clean up dataframe
post.variance <- melt(post.variance, id = c("Sex", "Environment"))
post.variance$variable <- NULL
make_text_summary(var.brms) %>% add_significance_stars() %>% tibble::rownames_to_column("Model Parameter") %>% pander()| Model Parameter | Estimate | Est.Error | Q2.5 | Q97.5 | |
|---|---|---|---|---|---|
| b_Intercept | 0.067 | 0.278 | -0.501 | 0.599 | |
| b_SexB | -0.048 | 0.036 | -0.118 | 0.023 | |
| b_SexF | -0.153 | 0.014 | -0.18 | -0.127 | * |
| b_EnvironmentStressed | -0.212 | 0.02 | -0.25 | -0.173 | * |
| b_SexB:EnvironmentStressed | -0.254 | 0.042 | -0.338 | -0.176 | * |
| b_SexF:EnvironmentStressed | -0.749 | 0.024 | -0.798 | -0.702 | * |
| sd_Outcome__Intercept | 0.448 | 0.131 | 0.27 | 0.77 | * |
| sd_Study.ID__Intercept | 0.393 | 0.049 | 0.31 | 0.503 | * |
| sd_Taxon__Intercept | 0.418 | 0.324 | 0.047 | 1.244 | * |
Predictions based on the REML and Bayesian model can then be generated in the same way as for Hedges’g. Here, negative values of lnCVR indicate a narrowing (decrease) in phenotypic variance as a result of sexual selection.
#Generate predictions
get.predictions.variance <- function(newdata){
B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
if(newdata[1] == "B") B<-1
if(newdata[1] == "F") F<-1
if(newdata[2] == "Stressed") Stressed<-1
if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1
predict(variance.model, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.variance <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed")))
predictions.variance <- cbind(predictions.variance, do.call("rbind", apply(predictions.variance, 1, get.predictions.variance))) %>%
select(Sex, Environment, pred, se, ci.lb, ci.ub)
for(i in 3:6) predictions.variance[,i] <- unlist(predictions.variance[,i])
countpred = count_(restricted.data2, c("Sex", "Environment"))
predictions.variance <- left_join(predictions.variance, countpred, by = c("Sex", "Environment"))
#Change names to make them more clear
predictions.variance <- predictions.variance %>%
mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
Sex = replace(Sex, Sex == "M", "Male"),
Sex = replace(Sex, Sex == "F", "Female")) %>%
mutate(Environment = replace(as.character(Environment), Environment == "Stressed", "Stressful"),
Environment = replace(Environment, Environment == "Unstressed", "Benign"))
colnames(predictions.variance) <- c("Sex", "Environment", "Prediction", "SE", "CI.lb", "CI.ub", "n")
#And plot the results, first for the posterior results of the brms model then for the metafor predictions
var.plot.posterior <- post.variance %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))) %>% ggplot()+
stat_density_ridges(aes(x=value, y = Environment, fill = Sex), alpha = 0.65, scale = 0.6, position = position_nudge(y = 0.15), height = 10, show.legend = F, quantile_lines = T, quantiles = 2)+
geom_vline(xintercept = 0, linetype = 2, colour = "black") +
ylab("Environment\n")+
xlab("\nPhenotypic Variance (lnCVR)")+
scale_x_continuous(limits = c(-2.1, 1.2), breaks = c(-2, -1.5, -1, -0.5, 0, 0.5, 1))+
scale_fill_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
scale_color_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
theme_bw()+
theme(panel.spacing = unit(0.1, "lines"),
text = element_text(size=16),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=16),
legend.title=element_text(size=16,
face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 14),
axis.title.y = element_text(size = 16, hjust = 0.35, margin = margin(r=-10)),
axis.text.y = element_text(angle = 0),
plot.title = element_text(size = 16))
both.var.plots <- var.plot.posterior +
geom_errorbarh(data = predictions.variance %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(x = Prediction, xmin = predictions.variance$CI.lb,
xmax = predictions.variance$CI.ub, y = Environment,
color = Sex),
height = 0, position = pd, show.legend = F) +
geom_point(data = predictions.variance %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(x = Prediction, y = Environment, size=n, fill = Sex),
shape=21, color = "grey20", position = pd) +
guides(fill = guide_legend(reverse=T, override.aes = list(size = 7.5)))+
scale_size(guide = 'none')+
scale_y_discrete(expand=c(0.075,0))
# svg("figures/both.var.plots.svg", width=6, height=6)
# both.var.plots
# dev.off()
#
# pdf("figures/both.var.plots.pdf", width = 6, height = 6)
# both.var.plots
# dev.off()
both.var.plots
Figure S4: Phenotypic variation changes under sexual selection in stressful environments. For females under stressful conditions phenotypic variation decreases (narrows). While for males in stressful environments it increases. For outcomes that measured a mix of both males and females (pooled samples) in stressful environments phenotypic variation decreased slightly. The REML predictions are shown as circles with error bars and the Bayesian predictions as density ridges. Circle size is proportional to sample size.
Table S14 The REML predictions in the plot above use the following dataframe.
predictions.variance <- format(predictions.variance, digits = 2)
predictions.variance %>% pander()| Sex | Environment | Prediction | SE | CI.lb | CI.ub | n |
|---|---|---|---|---|---|---|
| Male | Benign | 0.073 | 0.18 | -0.28 | 0.424 | 119 |
| Both | Benign | 0.026 | 0.18 | -0.33 | 0.381 | 15 |
| Female | Benign | -0.080 | 0.18 | -0.43 | 0.271 | 123 |
| Male | Stressful | -0.138 | 0.18 | -0.49 | 0.214 | 23 |
| Both | Stressful | -0.440 | 0.18 | -0.80 | -0.081 | 18 |
| Female | Stressful | -1.040 | 0.18 | -1.39 | -0.689 | 38 |
Hypothesis Tests For Phenotypic Variation Model
Like we did for Hedges g’ we can also conduct hypothesis tests between categorical groups to inform us if there is a difference between two groups of interest (e.g. females in benign and females in stressful environments). Here we present Bayesian and REML hypothesis predictions. Positive values indivate the first term in the hypothesis is larger (which would be male for rows 1-2 or benign for rows 3-5).
Table S15 Bayesian hypothesis tests between categorical groups for phenotypic variation (lnCVR)
#Obtain hypothesis estimates
brms.hypothesis.var <- hypothesis(var.brms, c("0 = SexF",
"0 = SexF + SexF:EnvironmentStressed",
"0 = SexF:EnvironmentStressed + EnvironmentStressed",
"0 = EnvironmentStressed",
"0 = SexB:EnvironmentStressed + EnvironmentStressed"))
#Format into dataframe
brms.hypothesis.table.var <-
data.frame(brms.hypothesis.var[["hypothesis"]][["Hypothesis"]],
brms.hypothesis.var[["hypothesis"]][["Estimate"]],
brms.hypothesis.var[["hypothesis"]][["Est.Error"]],
brms.hypothesis.var[["hypothesis"]][["CI.Lower"]],
brms.hypothesis.var[["hypothesis"]][["CI.Upper"]],
brms.hypothesis.var[["hypothesis"]][["Star"]])
colnames(brms.hypothesis.table.var) <- c("Hypothesis", "Estimate", "Est.Error", "CI.Lower", "CI.Upper", " ")
row.names(brms.hypothesis.table.var) <- c("M vs F, Benign", "M vs F, Stressful", "Benign vs Stressful, Female", "Benign vs Stressful, Male", "Benign vs Stressful, Both")
brms.hypothesis.table.var <- format(brms.hypothesis.table.var, digits = 2)
brms.hypothesis.table.var %>% pander(split.table = Inf)| Hypothesis | Estimate | Est.Error | CI.Lower | CI.Upper | ||
|---|---|---|---|---|---|---|
| M vs F, Benign | (0)-(SexF) = 0 | 0.15 | 0.014 | 0.13 | 0.18 | * |
| M vs F, Stressful | (0)-(SexF+SexF:EnvironmentStressed) = 0 | 0.90 | 0.022 | 0.86 | 0.95 | * |
| Benign vs Stressful, Female | (0)-(SexF:EnvironmentStressed+EnvironmentStressed) = 0 | 0.96 | 0.017 | 0.93 | 0.99 | * |
| Benign vs Stressful, Male | (0)-(EnvironmentStressed) = 0 | 0.21 | 0.020 | 0.17 | 0.25 | * |
| Benign vs Stressful, Both | (0)-(SexB:EnvironmentStressed+EnvironmentStressed) = 0 | 0.47 | 0.038 | 0.39 | 0.54 | * |
Table S16 REML hypothesis tests between categorical groups for phenotypic variation (lnCVR)
#anova where you specify the values based on the list of moderators
anova.1 = anova(variance.model, L=c(0, 0, -1, 0, 0, 0))
anova.2 = anova(variance.model, L=c(0, 0, -1, 0, 0, -1))
anova.3 = anova(variance.model, L=c(0, 0, 0, -1, 0, -1))
anova.4 = anova(variance.model, L=c(0, 0, 0, -1, 0, 0))
anova.5 = anova(variance.model, L=c(0, 0, 0, -1, -1, 0))
anova.list.var <- list(anova.1, anova.2, anova.3, anova.4, anova.5)
anova.frame.var <- t(data.frame(lapply(anova.list.var, function(x) {
data.frame(x[["hyp"]],
x[["Lb"]],
x[["se"]],
x[["Lb"]] - 1.96*x[["se"]],
x[["Lb"]] + 1.96*x[["se"]],
x[["pval"]])
})))
anova.frame.var <- as.data.frame(split(anova.frame.var, rep(1:6)))
colnames(anova.frame.var) <- c("Hypothesis", "Estimate", "Est.Error", "CI.Lower", "CI.Upper", "pval")
anova.frame.var$Estimate <- as.numeric(levels(anova.frame.var$Estimate))[anova.frame.var$Estimate]
anova.frame.var$Est.Error <- as.numeric(levels(anova.frame.var$Est.Error))[anova.frame.var$Est.Error]
anova.frame.var$CI.Lower <- as.numeric(levels(anova.frame.var$CI.Lower))[anova.frame.var$CI.Lower]
anova.frame.var$CI.Upper <- as.numeric(levels(anova.frame.var$CI.Upper))[anova.frame.var$CI.Upper]
anova.frame.var$pval <- as.numeric(levels(anova.frame.var$pval))[anova.frame.var$pval]
anova.frame.var <- format(anova.frame.var, digits = 2)
anova.frame.var$star <- c("*", "*", "*", "*", "*")
colnames(anova.frame.var)[colnames(anova.frame.var)=="star"] <- " "
anova.frame.var$pval <- NULL
row.names(anova.frame.var) <- c("M vs F, Benign", "M vs F, Stressful", "Benign vs Stressful, Female", "Benign vs Stressful, Male", "Benign vs Stressful, Both")
anova.frame.var %>% pander(split.table = Inf)| Hypothesis | Estimate | Est.Error | CI.Lower | CI.Upper | ||
|---|---|---|---|---|---|---|
| M vs F, Benign | -SexF = 0 | 0.15 | 0.013 | 0.13 | 0.18 | * |
| M vs F, Stressful | -SexF - SexF:EnvironmentStressed = 0 | 0.90 | 0.022 | 0.86 | 0.95 | * |
| Benign vs Stressful, Female | -EnvironmentStressed - SexF:EnvironmentStressed = 0 | 0.96 | 0.017 | 0.93 | 0.99 | * |
| Benign vs Stressful, Male | -EnvironmentStressed = 0 | 0.21 | 0.020 | 0.17 | 0.25 | * |
| Benign vs Stressful, Both | -EnvironmentStressed - SexB:EnvironmentStressed = 0 | 0.47 | 0.039 | 0.39 | 0.54 | * |
Estimating lnCVR Heterogeneity Using I2
Similar to the meta-analysis on Hedges’ g we can obtain I2 for lnCVR REML model. In this case (compared to Hedges’ g) we see Taxon has more of a variable effect on overall I2 estimates, although the overall I2 remains relatively similar (95.4 %).
I2(variance.model, restricted.data2$var.g) %>% pander(digits = 3)| I2_Est. | 2.5% CI | 97.5% CI | |
|---|---|---|---|
| Taxon | 16.6 | 3.1 | 37.2 |
| Study.ID | 40.1 | 23.5 | 60.2 |
| Outcome | 38.7 | 17.1 | 60.7 |
| total | 95.4 | 93 | 97.1 |
Publication Bias
Funnel plots and Egger’s Test
Here we check for publication bias with a funnel plot. Note that the trim and fill or Eggers test method does not work with rma.mv objects. We can perform Eggers test using the regtest() function. This tests for asymmetry via assessing relationships between effect size and a specified predictor. Because the Eggers test does not work for rma.mv objects we remove the random effects and run with Sex * Environment as moderators.
standard.model <- rma(g, var.g,
mods = ~ Sex * Environment,
data=prelim.data)
regtest(standard.model)##
## Regression Test for Funnel Plot Asymmetry
##
## model: mixed-effects meta-regression model
## predictor: standard error
##
## test for funnel plot asymmetry: z = 6.2210, p < .0001We can use ggplot for creating a funnel plot. The outline taken from is taken from: https://sakaluk.wordpress.com/2016/02/16/7-make-it-pretty-plots-for-meta-analysis/
#Using residuals for the funnel plot means that we need to generate residuals (intercept only)
forest.model <- rma.mv(g, var.g,
mods = ~ 1,
random = list(~ 1 | Study.ID,
~ 1 | Outcome),
method = "REML",
data = prelim.data)
# Obtain residuals
resstandards <- (rstandard.rma.mv(forest.model,
type="response"))
# Obtain grand mean effect size
grand.mean <- as.numeric(forest.model$b)
# Create new df with residuals replacing raw
df.forest.model <- prelim.data
df.forest.model$g <- resstandards$resid + grand.mean
df.forest.model$sei <- resstandards$se
# Funnel plot for all outcome classes
make.funnel <- function(dataset, model){
apatheme <- theme_bw() + #My APA-format theme
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.line = element_line(),
text = element_text(family = 'Times'),
legend.position = 'none')
estimate <- model$b
SE <- model$se
se.seq <- seq(0, max(sqrt(dataset$var.g)), 0.001)
dfCI <- data.frame(ll95 = estimate - (1.96 * se.seq),
ul95 = estimate + (1.96 * se.seq),
ll99 = estimate - (3.29 * se.seq),
ul99 = estimate + (3.29 * se.seq),
se.seq = se.seq,
meanll95 = estimate - (1.96 * SE),
meanul95 = estimate + (1.96 * SE))
ggplot(dataset, aes(x = sqrt(var.g), y = g)) +
geom_point(size=1.5, shape = 21, color= "grey20") +
xlab("Standard Error") + ylab("Effect Size (Hedges' g)") +
geom_line(aes(x = se.seq, y = ll95), linetype = 'dotted', data = dfCI) + # confidence lines
geom_line(aes(x = se.seq, y = ul95), linetype = 'dotted', data = dfCI) +
geom_line(aes(x = se.seq, y = ll99), linetype = 'dashed', data = dfCI) +
geom_line(aes(x = se.seq, y = ul99), linetype = 'dashed', data = dfCI) +
#Now plot dotted lines corresponding to the 95% CI of your meta-analytic estimate
geom_segment(aes(x = min(se.seq), y = meanll95, xend = max(se.seq), yend = meanll95), linetype='dotdash', data=dfCI, colour = "tomato",size =0.75) +
geom_segment(aes(x = min(se.seq), y = meanul95, xend = max(se.seq), yend = meanul95), linetype='dotdash', data=dfCI, colour = "tomato",size=0.75) +
scale_x_reverse() +
# scale_y_continuous(breaks = seq(-1.25,2,0.25)) + #Choose values that work for you based on your data
coord_flip() +
scale_fill_brewer(palette = "Set1")+
theme_bw()+
theme(panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
text = element_text(size=14),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12,
face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 12),
axis.title.y = element_text(size = 12))
}
funnel.plot <- make.funnel(df.forest.model, forest.model)
ggsave(plot = funnel.plot, filename = "figures/funnel_plot.eps", height = 7.5, width = 10)
funnel.plot
# svg("figures/funnel_plot.svg", width=8, height=6)
# funnel.plot
# dev.off()
Figure S5: A funnel plot of 459 effect sizes shows asymmetry, indicating potential publication bias, egger’s regression test for funnel plot asymmetry also suggests the plot is asymmetrical (z = 6.2210, p < .0001). The asymmetry appears to be sourced from a spread of positive effect sizes outside the funnel and of varying degrees of precision. Counter to expectations of publication bias these positive studies are not just ‘low precision, large effect’ results. Funnel plot asymmetry may also be due to heterogeneity, which in this study is high due to the many species and outcomes measured.
Journal Impact Factor
If we see a positive trend with effect size and Journal Impact Factor (JIF) it may represent publication bias whereby significant (positive) results are published more readily and in more circulated journals and non-confirmitory or negative results are not published or publiushed in lower impact journals. Our journal impact factor dataset is not evenly distributed as several publications in Nature (JIF ~ 40) are much larger than the next highest JIF (~11).
JIF.plot <- ggplot(data = prelim.data, aes(x=JIF, y=g))+
geom_jitter(color='darkgreen', alpha=0.4, aes(size = (1/(var.g))/sum((1/(var.g)))*100), show.legend = FALSE)+
geom_hline(yintercept=0, linetype = 'dotted')+
geom_smooth(method='lm', color='black', linetype="solid")+
scale_x_log10(limits = c(-5,40), breaks = c(0, 1, 2, 5, 10, 20, 40))+
labs(size = 'Weight (%)', y='Effect size (Hedges g)', x= 'Journal Impact Factor (logarithmic scale)')+
theme_bw()+
theme(panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
text = element_text(size=14),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12,
face = "bold"),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
# dev.off()
#
# svg("figures/JIF_Plot.svg", width=4, height=4)
# JIF.plot
# dev.off()
JIF.plot
Figure S6: Journal impact factor does not show a noticable correlation with effect size. The positive slope shown here is due to several effect sizes published in a high impact journal. Most papers were published in discipline specific journals such as Evolution and Journal of Evolutionary Biology. Circle size is proportional to weight (%) of study.
We can test the effect of JIF on ES with a simple linear model:
JIFlm <- lm(g ~ log(JIF), data = prelim.data)
summary(JIFlm)##
## Call:
## lm(formula = g ~ log(JIF), data = prelim.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6439 -0.3368 -0.1023 0.2561 2.9384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01680 0.13823 0.122 0.903
## log(JIF) 0.10850 0.09393 1.155 0.249
##
## Residual standard error: 0.6755 on 437 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.003044, Adjusted R-squared: 0.0007625
## F-statistic: 1.334 on 1 and 437 DF, p-value: 0.2487This shows that JIF does not have a significant effect on effect sizes from the published study
Time-lag Bias
We can also look at the time-lag bias, which suggests effect size decreases over time. Again, because one publication from 1980 is well before the next publication in the late 1990s we see a very uneven distribution of data points.
time.plot <- prelim.data %>%
ggplot(aes(x=Year, y=g))+
geom_jitter(color='darkorange', alpha=.5, aes(size = (1/(var.g))/sum((1/(var.g)))*100), show.legend = FALSE)+
geom_hline(yintercept=0, linetype = 'dotted')+
geom_smooth(method='lm', color='black')+
labs(size = 'Weight (%)', y='Effect size (Hedges g)', x= 'Year')+
theme_bw()+
theme(panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
axis.line=element_line(),
text = element_text(size=14),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12,
face = "bold"),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
time.plot
# svg("figures/Time_Plot.svg", width=4, height=4)
# time.plot
# dev.off()
Figure S7: The effect size dataset shows little to no signs of the time-lag bias as the average effect sizes from published studies remains consistent across the previous two decades. Circle size is proportional to weight of study (%).
We can also test the effect of publication year on ES with a simple linear model (similar to the one for JIF:
Yearlm <- lm(g ~ Year, data = prelim.data)
summary(Yearlm)##
## Call:
## lm(formula = g ~ Year, data = prelim.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6771 -0.3504 -0.1232 0.2934 2.9209
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.268558 12.784999 1.038 0.300
## Year -0.006507 0.006361 -1.023 0.307
##
## Residual standard error: 0.6942 on 457 degrees of freedom
## Multiple R-squared: 0.002285, Adjusted R-squared: 0.0001013
## F-statistic: 1.046 on 1 and 457 DF, p-value: 0.3069This shows that publication year does not have a significant effect on effect sizes from the published study.
Other Moderators
Blinding
In addition to publication bias, other forms of bias may exist within studies. We initially collected data on whether studies were blind or not. Although not many studies (n=8) used blinding there was multiple effect sizes reported in these studies, thus we can visualise whether blinding affects the effect sizes from the model. Blinding was regarded as a redundant predictor in the model (estimate = 0.0287, p = 0.8974) and was dropped.
blind.plot <- df.forest.model %>% ggplot(aes(x=Blinding, y=g))+
geom_boxplot(outlier.shape = NA)+
geom_jitter(aes(fill=Blinding, size = (1/(var.g))/sum((1/(var.g)))*100), shape=21, color='grey20')+
geom_hline(yintercept=0, linetype = 'dotted') +
scale_fill_brewer(palette = "Set2")+
labs(y="Effect size (Hedges' g)", x= 'Blinding', size = 'Weight (%)')+
guides(fill=FALSE, size = guide_legend(override.aes = list(fill = "#66c2a5")))+
theme_bw()+
theme(panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
axis.line=element_line(),
text = element_text(size=14),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12,
face = "bold"),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
#ggsave(plot = blind.plot, filename = "figures/blind_plot.eps", height = 6, width = 8)
blind.plot
Figure S9: Blinding does not appear to alter the magnitude or direction of effect sizes for the studies used in this meta-analysis. However, this should not be viewed as evidence against the validity of blinding as a research method.
Generations
We recorded the number of generations of experimental exolution each study used. The number of generations proved a negligible predictor in the meta-analytic models (estimate = 0.0019, p = 0.2341). The effect sizes are plotted against the generation at which the effect size was extracted.
generations.plot <- restricted.data %>% ggplot(aes(x=Generations, y=g))+
geom_jitter(shape=21, color = "grey20", size=2, aes(fill=Taxon))+
ylim(-3.5,3.5)+
geom_hline(yintercept=0, linetype="dashed") +
scale_fill_brewer(palette = "Set3")+
geom_smooth(method = 'lm', color='black')+
labs(y="Effect size (Hedges' g)", x= 'Generations', size= 'Weight (%)')+
theme_bw()+
theme(panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
axis.line=element_line(),
text = element_text(size=14),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12,
face = "bold"),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
#ggsave(plot = generations.plot, filename = "figures/generations_plot.eps", height = 7.5, width = 10)
# svg("figures/Generations_Plot.svg", width=10, height=7.5)
# generations.plot
# dev.off()
generations.plot
Figure S10: The number of generations an experimental evolution procedure is run for does not appear to affect the magnitude or direction of the effect size from the fitness related outcome measured at that point.
A linear model shows next to no effect of generations on effect size:
summary(lm(g ~ Generations, data = restricted.data))##
## Call:
## lm(formula = g ~ Generations, data = restricted.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7172 -0.3694 -0.1293 0.3132 2.6580
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2496047 0.0638614 3.909 0.000112 ***
## Generations -0.0003535 0.0015300 -0.231 0.817439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6679 on 334 degrees of freedom
## Multiple R-squared: 0.0001598, Adjusted R-squared: -0.002834
## F-statistic: 0.05337 on 1 and 334 DF, p-value: 0.8174Kawecki et al. (2012) reviewed the field of experimental evolution and noted that changes to variation may need longer generations to become apparent. The following graph looks at the relationship between number of generations and lnCVr:
generations.plot.var <- restricted.data2 %>% ggplot(aes(x=Generations, y=lnCVr))+
geom_jitter(shape=21, color = "grey20", size=2, aes(fill=Taxon))+
ylim(-3.5,3.5)+
geom_hline(yintercept=0, linetype="dashed") +
scale_fill_brewer(palette = "Set3")+
geom_smooth(method = 'lm', color='black')+
labs(y='Effect size (lnCVR)', x= 'Generations', size= 'Weight (%)')+
theme_bw()+
theme(panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
text = element_text(size=14),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12,
face = "bold"),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
# ggsave(plot = generations.plot, filename = "figures/generations_plot.eps", height = 7.5, width = 10)
generations.plot.var
Figure S11: Phenotypic variation (lnCVR) is not affected by the number of generations an experiment is ran for.
summary(lm(lnCVr ~ Generations, data = restricted.data2))##
## Call:
## lm(formula = lnCVr ~ Generations, data = restricted.data2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6285 -0.2647 0.0093 0.2299 3.1872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.122280 0.063333 -1.931 0.0545 .
## Generations 0.001404 0.001572 0.894 0.3724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5653 on 275 degrees of freedom
## (59 observations deleted due to missingness)
## Multiple R-squared: 0.002895, Adjusted R-squared: -0.0007311
## F-statistic: 0.7984 on 1 and 275 DF, p-value: 0.3724R Session Information
This section shows the operating system and R packages attached during the production of this document
sessionInfo() %>% panderR version 3.3.1 (2016-06-21)
**Platform:** x86_64-apple-darwin13.4.0 (64-bit)
locale: en_AU.UTF-8||en_AU.UTF-8||en_AU.UTF-8||C||en_AU.UTF-8||en_AU.UTF-8
attached base packages: grid, stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: bindrcpp(v.0.2), cowplot(v.0.9.1), glmulti(v.1.0.7), rJava(v.0.9-9), MuMIn(v.1.40.0), knitr(v.1.18), metaAidR(v.0.0.0.9000), brmstools(v.0.5.1), bayesplot(v.1.5.0), backports(v.1.1.2), brms(v.2.3.1), Rcpp(v.0.12.14), rstan(v.2.17.3), StanHeaders(v.2.17.2), ggridges(v.0.5.0), RColorBrewer(v.1.1-2), reshape2(v.1.4.3), ggrepel(v.0.7.0), kableExtra(v.0.7.0), ggthemes(v.3.4.0), ggplot2(v.2.2.1), forestplot(v.1.7.2), checkmate(v.1.8.5), magrittr(v.1.5), car(v.2.1-5), lme4(v.1.1-15), dplyr(v.0.7.4), plyr(v.1.8.4), metafor(v.2.0-0), Matrix(v.1.2-6), compute.es(v.0.2-4), tidyr(v.0.7.2) and pander(v.0.6.1)
loaded via a namespace (and not attached): nlme(v.3.1-128), matrixStats(v.0.53.1), pbkrtest(v.0.4-7), xts(v.0.10-1), threejs(v.0.3.1), httr(v.1.3.1), rprojroot(v.1.3-2), tools(v.3.3.1), R6(v.2.2.2), DT(v.0.4), lazyeval(v.0.2.1), mgcv(v.1.8-12), colorspace(v.1.3-2), nnet(v.7.3-12), gridExtra(v.2.3), Brobdingnag(v.1.2-4), rvest(v.0.3.2), quantreg(v.5.33), SparseM(v.1.77), shinyjs(v.1.0), xml2(v.1.1.1), labeling(v.0.3), colourpicker(v.1.0), scales(v.0.5.0), dygraphs(v.1.1.1.4), mvtnorm(v.1.0-6), readr(v.1.1.1), stringr(v.1.2.0), digest(v.0.6.13), minqa(v.1.2.4), rmarkdown(v.1.8), base64enc(v.0.1-3), pkgconfig(v.2.0.1), htmltools(v.0.3.6), htmlwidgets(v.1.2), rlang(v.0.1.6), shiny(v.1.0.5), bindr(v.0.1), zoo(v.1.8-1), gtools(v.3.5.0), crosstalk(v.1.0.0), inline(v.0.3.14), loo(v.2.0.0), munsell(v.0.4.3), abind(v.1.4-5), stringi(v.1.1.5), yaml(v.2.1.16), MASS(v.7.3-45), parallel(v.3.3.1), miniUI(v.0.1.1), lattice(v.0.20-33), splines(v.3.3.1), hms(v.0.3), igraph(v.1.1.2), markdown(v.0.8), shinystan(v.2.4.0), codetools(v.0.2-14), stats4(v.3.3.1), rstantools(v.1.5.0), glue(v.1.1.1), evaluate(v.0.10.1), nloptr(v.1.0.4), httpuv(v.1.3.5), MatrixModels(v.0.4-1), gtable(v.0.2.0), purrr(v.0.2.4), assertthat(v.0.2.0), mime(v.0.5), xtable(v.1.8-2), coda(v.0.19-1), rsconnect(v.0.8.8), viridisLite(v.0.2.0), tibble(v.1.3.4), shinythemes(v.1.1.1) and bridgesampling(v.0.4-0)
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